Reduction of back pattern dependency effects in memory devices

Abstract
A method for operating a memory that includes multiple analog memory cells includes storing data in the memory by writing first storage values to the cells, so as to cause the cells to hold respective electrical charge levels. After storing the data, second storage values are read from at least some of the cells, including at least one interfered cell that belongs to a group of cells. A Back Pattern Dependency (BPD) distortion caused by the electrical charge levels of one or more interfering cells in the group to at least one of the second storage values read from the at least one interfered cell is detected and canceled. The second storage values, including the at least one of the second storage values in which the BPD distortion was canceled, are processed so as to reconstruct the data.
Description
FIELD OF THE INVENTION

The present invention relates generally to memory devices, and particularly to methods and systems for mitigating Back Pattern Dependency (BPD) effects in memory devices.


BACKGROUND OF THE INVENTION

Several types of memory devices, such as Flash memories, use arrays of analog memory cells for storing data. Each analog memory cell stores a quantity of an analog value, also referred to as a storage value, such as an electrical charge. The storage value represents the information stored in the cell. In Flash memories, for example, each analog memory cell holds a certain amount of electrical charge. The range of possible analog values is typically divided into regions, each region corresponding to one or more data bit values. Data is written to an analog memory cell by writing a nominal analog value that corresponds to the desired bit or bits.


Some memory devices, commonly referred to as Single-Level Cell (SLC) devices, store a single bit of information in each memory cell, i.e., each memory cell can be programmed to assume two possible memory states. Higher-density devices, often referred to as Multi-Level Cell (MLC) devices, store two or more bits per memory cell, i.e., can be programmed to assume more than two possible memory states.


Flash memory devices are described, for example, by Bez et al., in “Introduction to Flash Memory,” Proceedings of the IEEE, volume 91, number 4, April, 2003, pages 489-502, which is incorporated herein by reference. Multi-level Flash cells and devices are described, for example, by Eitan et al., in “Multilevel Flash Cells and their Trade-Offs,” Proceedings of the 1996 IEEE International Electron Devices Meeting (IEDM), New York, N.Y., pages 169-172, which is incorporated herein by reference. The paper compares several kinds of multilevel Flash cells, such as common ground, DINOR, AND, NOR and NAND cells.


In some memory device types, memory cells are arranged in cell strings. Each cell string comprises a group of cells that are connected back-to-back in series with one another. Cell strings are typically used, for example, in NAND Flash devices. NAND Flash devices are described, for example, by Jung et al., in “A 117 mm2 3.3 Volts Only 128-Mb Multilevel NAND Flash Memory for Mass Storage Applications,” IEEE Journal of Solid State Circuits, (11:31), November, 1996, pages 1575-1583, and by Takeuchi et al., in “A Double Level VTH Select Gate Array Architecture for Multi-Level NAND Flash Memories,” Digest of Technical Papers, 1995 Symposium on VLSI Circuits, Jun. 8-10, 1995, pages 69-70, which are incorporated herein by reference.


Memory cells that are arranged in cell strings sometimes suffer from Back Pattern Dependency (BPD) distortion. BPD distortion in a given cell is typically caused by changes in the conduction properties of other cells in the string, which cause an artificial shift in the storage value read from the given cell. U.S. Pat. No. 7,310,272, whose disclosure is incorporated herein by reference, describes a memory device that accounts for back pattern effects by applying a first voltage during a verify operation for unselected word lines that have been subjected to a programming operation, and a second voltage for unselected word lines that have not been subjected to a programming operation.


SUMMARY OF THE INVENTION

Embodiments of the present invention provide a method for operating a memory that includes multiple analog memory cells, the method including:


storing data in the memory by writing first storage values to the cells, so as to cause the cells to hold respective electrical charge levels;


after storing the data, reading second storage values from at least some of the cells, including at least one interfered cell that belongs to a group of cells;


detecting and canceling a Back Pattern Dependency (BPD) distortion caused by the electrical charge levels of one or more interfering cells in the group to at least one of the second storage values read from the at least one interfered cell; and


processing the second storage values, including the at least one of the second storage values in which the BPD distortion was canceled, so as to reconstruct the data.


In some embodiments, the analog memory cells include NAND Flash cells, and the group of cells includes a NAND string. In an embodiment, detecting and canceling the BPD distortion include evaluating a condition indicating that the interfered cell is likely to be subject to the BPD distortion, and canceling the BPD distortion responsively to the evaluated condition. In another embodiment, storing the data includes encoding the data with an error detection code, and evaluating the condition includes decoding the error detection code and identifying a data error using the decoded error detection code. In yet another embodiment, reading the second storage values includes processing the read second storage values so as to identify one or more over-programmed cells among the memory cells in the memory, and evaluating the condition includes determining that the group of cells includes at least one of the over-programmed cells.


In a disclosed embodiment, detecting and canceling the BPD distortion include recursively estimating a part of the BPD distortion in a given cell in the group based on one or more other parts of the BPD distortion in respective one or more cells in the group that were programmed earlier than the given cell, and on the second storage values that were read from the one or more cells. Estimating the part of the BPD distortion may include measuring a total BPD distortion that is caused by the cells in the group, and estimating the part of the BPD distortion based on the total BPD distortion. In some embodiment, the cells in the group are connected in series to one another, and measuring the total BPD distortion includes applying pass voltages to respective gates of the cells in the group and measuring a current flowing through the cells responsively to the applied pass voltages.


In an embodiment, the cells in the group are connected in series to one another, reading the second storage value from the interfered cell includes applying a read voltage to a gate of the interfered cell, applying pass voltages to respective gates of the other cells in the group and measuring a current flowing through the cells responsively to the applied read voltage and pass voltages, and canceling the BPD distortion includes increasing at least one of the pass voltages and reading the second storage value from the interfered cell using the increased pass voltages. Increasing the pass voltages may include increasing the pass voltages of only a subset of the cells in the group that were programmed later than the interfered cell. Increasing the pass voltages may include increasing the pass voltages iteratively until meeting a condition indicating that the BPD distortion is canceled. In an embodiment, increasing the pass voltages includes storing the pass voltages for which the condition was met, and applying the stored pass voltages when performing a subsequent read operation on the group of cells.


In some embodiments, after reading the second storage value from the interfered cell using the increased pass voltages, the data stored in the group is copied to another group of the memory cells. In an embodiment, reading the second storage value from the interfered cell includes reading the second storage value multiple times while applying respective different values of the pass voltages to the gates of the other cells in the group, and canceling the BPD distortion includes selecting one of the read second storage values having a lowest level of the BPD distortion.


In a disclosed embodiment, the cells in the group are connected in series to one another, reading the second storage value from the interfered cell includes applying a read voltage to a gate of the interfered cell, applying pass voltages to respective gates of the other cells in the group and measuring a current flowing through the cells responsively to the applied read voltage and pass voltages, and canceling the BPD distortion includes reducing at least one of the pass voltages while not violating a predefined performance criterion, and reading the second storage value from the interfered cell using the reduced pass voltages.


In another embodiment, reading the second storage values includes processing the read second storage values so as to identify one or more over-programmed cells among the memory cells in the memory and storing information regarding the identified over-programmed cells, and canceling the BPD distortion includes processing the stored information regarding the over-programmed cells with regard to the read second storage values so as to cancel the BPD distortion. Processing the stored information may include estimating the BPD distortion based on the stored information and subtracting the estimated BPD distortion from the second storage value read from the interfered cell. In yet another embodiment, storing the data includes encoding the data with an Error Correction Code (ECC), processing the stored information includes calculating metrics for decoding the ECC based on the stored information, and processing the second storage values so as to reconstruct the data includes decoding the ECC responsively to the metrics.


In some embodiments, detecting the BPD distortion includes measuring shifts in one or more of the second storage values read from a subset of the cells in the group, which occurred after the cells were programmed, and estimating the BPD distortion responsively to the measured shifts. In an embodiment, storing the data includes encoding the data with an Error Correction Code (ECC), canceling the BPD distortion includes computing metrics for decoding the ECC based on the detected BPD distortion, and processing the second storage values so as to reconstruct the data includes decoding the ECC responsively to the metrics.


There is additionally provided, in accordance with an embodiment of the present invention, a data storage apparatus, including:


an interface, which is operative to communicate with a memory that includes multiple analog memory cells; and


a processor, which is coupled to store data in the memory by writing first storage values to the cells so as to cause the cells to hold respective electrical charge levels, to read, after storing the data, second storage values from at least some of the cells, including at least one interfered cell that belongs to a group of cells, to detect and cancel a Back Pattern Dependency (BPD) distortion caused by the electrical charge levels of one or more interfering cells in the group to at least one of the second storage values read from the at least one interfered cell, and to process the second storage values, including the at least one of the second storage values in which the BPD distortion was canceled, so as to reconstruct the data.


There is further provided, in accordance with an embodiment of the present invention, a data storage apparatus, including:


a memory, which includes multiple analog memory cells; and


a processor, which is coupled to store data in the memory by writing first storage values to the cells so as to cause the cells to hold respective electrical charge levels, to read, after storing the data, second storage values from at least some of the cells, including at least one interfered cell that belongs to a group of cells, to detect and cancel a Back Pattern Dependency (BPD) distortion caused by the electrical charge levels of one or more interfering cells in the group to at least one of the second storage values read from the at least one interfered cell, and to process the second storage values, including the at least one of the second storage values in which the BPD distortion was canceled, so as to reconstruct the data.


The present invention will be more fully understood from the following detailed description of the embodiments thereof, taken together with the drawings in which:





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 is a block diagram that schematically illustrates a memory cell array, in accordance with an embodiment of the present invention;



FIG. 2 is a block diagram that schematically illustrates a memory system, in accordance with an embodiment of the present invention; and



FIGS. 3-6 are flow charts that schematically illustrate methods for estimating and correcting Back Pattern Dependency (BPD) effects, in accordance with embodiments of the present invention.





DETAILED DESCRIPTION OF EMBODIMENTS
Overview

Embodiments of the present invention provide methods and systems for canceling Back Pattern Dependency (BPD) distortion in arrays of analog memory cells. In the embodiments that are described hereinbelow, a Memory Signal Processor (MSP) stores data in a memory that includes multiple analog memory cells by writing respective storage values to the cells, thus causing the cells to hold respective electrical charge levels. In order to read the data, the MSP reads the storage values from at least some of the cells.


The memory may contain at least one interfered memory cell, whose storage value is distorted by BPD. An interfered cell of this sort belongs to a certain group of cells, and the storage value read from the cell is distorted by BPD that is caused by one or more electrical charge levels of other cells in the group. In some embodiments, the memory cells comprise NAND Flash cells, and the group of cells comprises a NAND string. The MSP cancels the BPD distortion in the storage value read from the interfered cell, and processes the read storage values to reconstruct the data.


Several methods for canceling BPD distortion are described in detail hereinbelow. Since in many memory devices BPD distortion is severe in only a relatively small number of cells, the MSP typically invokes these methods only when necessary. For example, the MSP may encode the stored data with an Error Correction Code (ECC), and apply BPD cancellation in response to a failure of the ECC decoding process.


In some cases, as will be explained below, the BPD distortion affecting a cell in a certain row (word line) of the array is caused primarily by cells in higher rows. In some embodiments, the MSP estimates the BPD distortion in a recursive calculation, which calculates the BPD level in each row based on the previously-calculated BPD of the lower rows and on the data read from these rows.


In some embodiments, when a cell that belongs to a given NAND string is read, certain pass voltages are applied to the other word lines associated with the string. In a disclosed method, the MSP increases the pass voltages in order to reduce the BPD level. The MSP typically increases the pass voltages only for NAND strings in which BPD is suspected, so as to minimize undesirable effects that are associated with high pass voltages.


In many cases, BPD is caused primarily by over-programmed memory cells. In another disclosed method, the MSP identifies over-programmed cells, stores information regarding the identified over-programmed cells, and uses the stored information to reduce BPD when reading the interfered cells. BPD may also be caused by shifts in the charge of the interfering cells, which occurred after the interfered cells were programmed. In yet another method, the MSP estimates the threshold voltage shifts and cancels the BPD based on the estimated shifts.


The BPD cancellation methods and systems described herein increase the capacity and the data storage reliability of analog memory cell arrays. The methods and systems described herein also enable reducing the pass voltages used in analog memory cell arrays, thus reducing power dissipation, the severity of program disturbs and/or memory cell wearing.


Memory Cell Array Configuration


FIG. 1 is a block diagram that schematically illustrates a memory cell array 20, in accordance with an embodiment of the present invention. Array 20 comprises a NAND Flash array, which comprises multiple analog memory cells 24. Each cell 24 comprises a floating gate Metal-Oxide Semiconductor (MOS) transistor. A certain amount of electrical charge (electrons or holes) can be stored in a particular cell by applying appropriate voltage levels to the transistor gate, source and drain. The value stored in the cell can be read by measuring the threshold voltage of the cell, which is defined as the minimal voltage that needs to be applied to the gate of the transistor in order to cause the transistor to conduct. The read threshold voltage is indicative of the charge stored in the cell.


Memory cells 24 of array 20 are arranged in a grid having multiple rows and columns. The gates of the transistors in each row are connected by word lines 28, and the transistors in each column are connected to bit lines 32. The transistors in a given column are arranged in groups, which are referred to as NAND strings 36. A given NAND string can be selected (i.e., connected to the bit line) by a string select switch 38. In a given string 28, the transistors are connected source-to-drain in series with one another. The first transistor in the string is connected to the bit line via switch 38. The last transistor is connected to ground or to another suitable baseline voltage. Each NAND string typically comprises thirty-two transistors, although any other suitable NAND string size can be used.


Array 20 is typically divided into multiple pages, i.e., groups of memory cells that are programmed and read simultaneously. Pages are sometimes sub-divided into sectors. In some embodiments, each page comprises an entire row of the array. In alternative embodiments, each row (word line) can be divided into two or more pages. For example, in some SLC devices each row is divided into two pages, one comprising the odd-order cells and the other comprising the even-order cells. In a typical implementation, a two-bit-per-cell memory device may have four pages per row, a three-bit-per-cell memory device may have six pages per row, and a four-bit-per-cell memory device may have eight pages per row.


Cells 24 are typically erased in blocks that contain multiple pages. Typically, erasure blocks comprise entire NAND strings, i.e., all cells of a given NAND string are typically erased en-bloc. Array 20 may comprise several thousand erasure blocks. In a typical two-bit-per-cell MLC device, each erasure block is on the order of thirty-two word lines, each comprising several thousand cells. Each word line of such a device is often partitioned into four pages (odd/even order cells, least/most significant bit of the cells). Three-bit-per cell devices having thirty-two word lines per erasure block would have 192 pages per erasure block, and four-bit-per-cell devices would have 256 pages per block. Alternatively, other block sizes and configurations can also be used.


Some memory devices comprise two or more separate memory cell arrays, often referred to as planes. Since each plane has a certain “busy” period between successive write operations, data can be written alternately to the different planes in order to increase programming speed.


System Description


FIG. 2 is a block diagram that schematically illustrates a memory system 40, in accordance with an embodiment of the present invention. System 40 can be used in various host systems and devices, such as in computing devices, cellular phones or other communication terminals, removable memory modules (“disk-on-key” devices), digital cameras, music and other media players and/or any other system or device in which data is stored and retrieved.


System 40 comprises a memory device 42, which stores data in a memory cell array 44. The memory array comprises multiple analog memory cells 46. In the context of the present patent application and in the claims, the term “analog memory cell” is used to describe any memory cell that holds a continuous, analog value of a physical parameter, such as an electrical voltage or charge. In the present example, array 44 comprises a NAND Flash array, whose memory cells are arranged in NAND strings, such as in the configuration of FIG. 1 above. Alternatively, array 44 may comprise other kinds of analog memory cells, such as, for example, NOR, AND or DINOR Flash cells.


The charge levels stored in the cells and/or the analog voltages or currents of the cells that result from programming the cells are referred to herein collectively as analog values or storage values. Although the embodiments described herein mainly address threshold voltages, the methods and systems described herein may be used with any other suitable kind of storage values. System 40 stores data in the analog memory cells by programming the cells to assume respective memory states, which are also referred to as programming levels. The memory states are selected from a finite set of possible states, and each state corresponds to a certain nominal storage value. For example, a 2 bit/cell MLC can be programmed to assume one of four possible memory states by writing one of four possible nominal storage values into the cell.


Memory device 42 comprises a reading/writing (R/W) unit 48, which converts data for storage in the memory device to analog storage values and writes them into memory cells 46. In alternative embodiments, the R/W unit does not perform the conversion, but is provided with voltage samples, i.e., with the storage values for storage in the cells. When reading data out of array 44, R/W unit 48 converts the storage values of memory cells 46 into digital samples having a resolution of one or more bits. In order to perform read and write operations, R/W unit 48 applies the appropriate voltages to the word lines and bit lines of array 44, and operates the string-select switches of the array.


The storage and retrieval of data in and out of memory device 28 is performed by a Memory Signal Processor (MSP) 52. MSP 52 communicates with a host 56, for accepting data for storage in the memory device and for outputting data retrieved from the memory device. MSP 52 comprises an interface 60 for communicating with memory device 42, and a signal processing unit 64, which processes the data that is written into and read from device 42. In some embodiments, unit 64 encodes the data for storage using an Error Correction Code (ECC), and decodes the ECC of the data retrieved from the memory.


In some embodiments, unit 64 produces the storage values for storing in the memory cells and provides these values to R/W unit 48. In alternative embodiments, unit 64 provides the data for storage, and the conversion to storage values is carried out by the R/W unit internally to the memory device. MSP 52, and in particular unit 64, may be implemented in hardware. Alternatively, MSP 52 may comprise a microprocessor that runs suitable software, or a combination of hardware and software elements.


The configuration of FIG. 2 is an exemplary system configuration, which is shown purely for the sake of conceptual clarity. Any other suitable configuration can also be used. Elements that are not necessary for understanding the principles of the present invention, such as various interfaces, addressing circuits, timing and sequencing circuits and debugging circuits, have been omitted from the figure for clarity.


In the exemplary system configuration shown in FIG. 2, memory device 42 and MSP 52 are implemented as two separate Integrated Circuits (ICs). In alternative embodiments, however, the memory device and MSP may be integrated on separate semiconductor dies in a single Multi-Chip Package (MCP) or System on Chip (SoC). Further alternatively, some or all of the MSP circuitry may reside on the same die on which the memory array is disposed. Further alternatively, some or all of the functionality of MSP 52 can be implemented in software and carried out by a processor or other element of the host system. In some implementations, a single MSP 52 may be connected to multiple memory devices 42.


Back Pattern Dependency Effects

In order to read data from a particular memory cell, the R/W unit senses the cell's threshold voltage. The threshold voltage of a cell is commonly defined as the lowest voltage that should be applied to the cell's gate in order to cause the current between the cell's source and drain to exceed a certain value. The threshold voltage of a cell depends on the charge accumulated in its floating gate. When the desired cell is part of a NAND string, the threshold voltage of the desired cell (also referred to as the selected cell) is to be sensed, while minimizing the effects of other cells (unselected cells) in the same NAND string. In a typical read operation, the R/W unit applies suitable voltages to the word lines of the cells along the NAND string, and senses the current that flows in the bit line. (Although the description that follows refers to a single NAND string for the sake of clarity, the reading process is usually performed concurrently on multiple NAND strings.)


The R/W unit applies a certain read voltage, which depends on the read reference level, to the word line of the selected cell. The R/W unit applies another voltage, commonly referred to as a pass voltage denoted VPASS, to the other word lines of the string. The pass voltage is typically a high voltage that is intended to cause the unselected cells to conduct, and to have source-drain voltage drops that are independent of their charge levels. In other words, the pass voltage is intended to cause the bit line current to reflect the charge level of the selected cell, and to be insensitive to the charge levels of the unselected cells.


In some embodiments, the R/W unit applies the appropriate word line voltages, pre-charges the bit line, and then connects the NAND string of the cell to the bit line using the appropriate string-select switch, thus causing the bit line to discharge via the NAND string. The R/W unit measures the bit line current or voltage after a certain period of time, and determines the threshold voltage of the selected cell based on the measurement. Alternatively, the R/W unit may use any other method for measuring the threshold voltage of the selected cell by sensing the bit line.


In practice, however, the bit line current is sometimes affected not only by the threshold voltage of the selected cell, but also by the charge levels of other cells in the NAND string. As a result, the bit line current sensed by the R/W unit may be distorted and may not truly reflect the charge level of the selected cell. The distortion caused to the sensed threshold voltage of the selected cell by charge levels of unselected cells in the NAND string is referred to as Back Pattern Dependency (BPD).


BPD may be produced, for example, when the conductivity of an unselected cell is not perfectly independent of its charge level, in spite of the pass voltage applied to its word line. In particular, when a certain cell is over-programmed, i.e., when its charge level is exceedingly high, the cell may not conduct properly even when a high pass voltage is applied to its word line. Thus, over-programmed cells are a major source of BPD.


Analog memory cells are often programmed using an iterative Program and Verify (P&V) process, as is known in the art. In a typical P&V process, an incrementally-increasing sequence of programming pulses is applied to the cells. The cell voltages are read between successive programming iterations and verified against the desired programming levels. When the cells are programmed using a P&V process, the iterative verification process may inherently compensate for some of the BPD distortion.


The P&V process may compensate for BPD caused by unselected cells whose charge levels did not change substantially between the time the selected cell is programmed and the time the selected cell is read. Thus, in some cases, residual BPD is caused primarily by unselected cells that were programmed later than the selected cell. In some memory devices, the cells are programmed in an ascending order of word lines. In these applications, BPD is caused primarily by cells in word lines that are higher than the word line of the selected cell. BPD may also be caused by cells whose charge levels have changed over time, for example because of aging, program disturbs or read disturbs.


BPD Estimation and Correction Methods

Embodiments of the present invention provide methods and systems for estimating and correcting BPD distortion effects. The methods described herein are carried out by MSP 52. In some embodiments, the MSP encodes the stored data with an ECC. Since BPD distortion is usually significant only in a relatively small number of cells in the array, the ECC decoding process of the MSP is often capable of correcting data errors that may be caused by BPD without explicitly detecting and correcting the actual BPD-related distortion. Thus, in some embodiments, the MSP invokes the methods described below only when necessary, e.g., in response to a failure of the ECC decoding process.



FIG. 3 is a flow chart that schematically illustrates a method for estimating and correcting BPD effects, in accordance with an embodiment of the present invention. The method of FIG. 3 assumes, as explained above, that the BPD distortion that affects a selected cell belonging to a certain word line is caused by the cells in higher word lines. The BPD level is estimated in a recursive calculation, which calculates the BPD level in a certain word line based on the previously-calculated BPD of the lower word lines and on the programming levels of the string cells located in these word lines.


The method begins with the R/W unit applying a pass voltage VPASS to all the word lines of the NAND string in question, at a pass voltage application step 70. When VPASS is applied to all the word lines, the MSP measures the total amount of BPD caused by the cells in the NAND string, at a total BPD measurement step 74. The MSP senses the bit line current or voltage under these conditions, and estimates the total BPD level.


The MSP may measure the total BPD level, for example, by pre-charging the bit line that is connected to the string, and measuring the time required for the bit line to discharge through the cells of the string when VPASS is applied to the gates of all the string's cells.


The measured total BPD value, denoted BPDTOTAL, is used as an estimate of the BPD that affects the lowest word line in the string, denoted WL0. (After performing step 70, the MSP can decide which strings suffer from severe BPD and are to undergo BPD cancellation and which strings have tolerable BPD levels.)


The MSP now estimates the BPD contributed by the other word lines, at a BPD estimation step 78. The BPD level distortion in the NAND string cell that belongs to word line WLi can be estimated based on the BPD contributions of the cells in the lower word lines and on the data (programming level) read from the lower word lines by







BPD
WLi

=


BPD
TOTAL

-




j
=
0


i
-
1








f


(

LEVEL
WLj

)









wherein BPDWLi denotes the BPD distortion caused to the NAND string cell in word line i, LEVELWLj denotes the programming level of the NAND string cell of word line j, and f(x) denotes a function that estimates the expected BPD caused by a cell whose programming level is x. Generally, LEVELWLj may refer to the analog storage value read from the cell at word line j, or to the programming state (programming level) to which this value corresponds.


Thus, the MSP first reads the programming level of the cell in word line WL0, estimates the BPD distortion in the cell of word line WL1 (denoted BPDWL1) based on the total BPD and on the level read from word line WL0, and so on.


Although the example above refers to a scenario in which the memory is programmed in ascending order of word lines, the method of FIG. 3 can be used in other scenarios, as well. Generally, the MSP can carry out the recursive process of FIG. 3 and estimate the BPD contribution of a given cell based on the BPD contributions of the cells that were programmed before the given cell, and on the data read from these cells.


Alternatively, the BPD distortion in the cell of word line i can be estimated by reading the levels of the cells in word lines i+1, i+2, . . . N, wherein N denotes the index of the last word line in the string, and calculating







BPD
WLi

=




j
=

i
+
1


N







f


(

LEVEL
WLj

)







The estimated BPD distortion levels can be subtracted from the storage values read from the cells. Alternatively, the estimated distortion levels can be taken into account when calculating likelihood metrics of the data stored the cells. Such metrics may subsequently be used for soft decoding of an Error Correction Code (ECC) that encodes the data.



FIG. 4 is a flow chart that schematically illustrates another method for identifying and correcting BPD effects, in accordance with an alternative embodiment of the present invention. The method of FIG. 4 identifies memory pages that are suspected of suffering from BPD, and in response increases the pass voltages applied to at least some of the unselected word lines in the NAND strings of these pages.


The method begins with system 40 intending to read a certain target memory page, which is stored in a group of memory cells along a certain word line in the memory cell array. The word line belongs to a certain set of NAND strings. The R/W unit applies a default value of the pass voltage VPASS to the unselected word lines of the NAND strings, at a default VPASS application step 80. The MSP then reads the memory page, at a reading step 84.


The MSP checks whether the read page is suspected of suffering from BPD, at a BPD checking step 88. The MSP can evaluate any suitable condition that indicates whether a page is likely to suffer from BPD. For example, the MSP may regard any page whose ECC decoding fails as a suspect. Alternatively, the MSP may encode the data with an error detection code, such as a Cyclic Redundancy Check (CRC), and regard read pages whose CRC fails as suspects. In the present context, an ECC is also considered to be a type of error detection code.


Additionally or alternatively, the MSP may maintain information regarding locations of over-programmed cells in the memory array. The MSP may regard a page as a suspect if the unselected word lines belonging to the same NAND strings as the page contain over-programmed cells. In some embodiments, the MSP may consider only over-programmed cells that were programmed later than the page in question (e.g., cells that are located in word lines that are higher than the word line of the read page). Several exemplary techniques for collecting and storing information regarding over-programmed cells are described further below.


If the read operation is successful or if the read page is not suspected of suffering from BPD, the MSP regards the page as successfully-read, at a read success step 92. Otherwise, the MSP initiates an iterative process that gradually increases the pass voltages of some or all of the unselected cells, attempting to reduce the BPD level.


The MSP checks whether a maximum allowed number of iterations has been performed, at an iteration number checking step 96. If the maximum number of iterations has already been performed and the page is still suspected of suffering from BPD, the MSP declares a read failure, at a read failure step 100.


Otherwise, the MSP instructs the R/W unit to increase the pass voltages applied to unselected word lines, at a VPASS increasing step 104. The R/W unit increases the pass voltages, such as by a predetermined step size. For example, the default pass voltage may be set at 6V, and the pass voltage may be increased by 0.5V in each iteration. Alternatively, any other suitable default value and step size can be used. The step size may be fixed or variable. After increasing the pass voltages, the method loops back to reading step 84 above. The MSP re-reads the page with the increased pass voltages, attempting to reduce the BPD level.


In some embodiments, the maximum number of iterations allowed in step 96 is two. In these embodiments, the pass voltage is increased only once and the page is re-read only once.


In some embodiments, the MSP increases only the pass voltages applied to the word lines that were programmed later than the read page, assuming that BPD may only be caused by cells in those word lines. Word lines that were programmed before the page in question are read with the default pass voltage. Alternatively, the MSP may increase the pass voltages of all unselected word lines, or of any desired subset of the unselected word lines. For example, when information regarding the locations of over-programmed cells is available, the MSP may increase only the pass voltages of word lines that contain over-programmed cells.


Increasing pass voltages may sometimes have undesirable effects. For example, higher pass voltages may increase the power dissipation of the memory device, cause higher read disturbs and/or increase the wearing of cells. Since the method of FIG. 4 increases the pass voltages selectively, only for word lines that are suspected of causing BPD, these undesired effects can be reduced. Since BPD effects are relatively rare, only a small fraction of the total number of word lines in the device will typically have their pass voltages increased using the method of FIG. 4.


In some embodiments, the R/W unit and the MSP carry out the method of FIG. 4 by performing multiple read operations on a given page, each read operation performed with a different pass voltage value. In these embodiments, the MSP selects the result of the read operation that is most likely to have the lowest BPD. This technique is useful, for example, in memory device configurations that do not enable applying different pass voltages simultaneously to different word lines. The MSP may select the result based on any suitable criteria, such as on the data values read from the desired page or from cells in the unselected word lines.


In some embodiments, when a certain VPASS value is known to perform well in a given group of cells (e.g., an erasure block), the MSP stores this VPASS value for future use. For example, when the method of FIG. 4 terminates successfully at step 92, the MSP stores the VPASS value that was used in the last iteration. The value may be stored in one or more of the memory cells of the group, or in any other suitable location. If the MSP reads data again from this group of cells at a later time, the stored value can be used as the default value.


In some embodiments, after reading the page using the increased pass voltages, the MSP copies the data to another page and resets the pass voltages to their default values. Using this technique, the data can be accessed again at a later time if necessary, without the need to use high pass voltages. Alternatively, the MSP may copy the data of the entire erasure block to another block, or re-program the data in the same cells (typically after erasing the block).


The technique of modifying the pass voltages responsively to the BPD level can also be used for reducing the pass voltages, and thus reducing power dissipation, program disturbs and wearing of cells. For example, the MSP may gradually reduce the pass voltages applied to the unselected word lines, as long as the reading performance remains acceptable, i.e., as long as a certain performance-related criterion is not violated. For example, the MSP may reduce VPASS until read errors begin to appear or until the Bit Error Rate (BER) of the read data reaches a certain threshold. Using this method, each group of cells (e.g., each erasure block) can be read using the minimal pass voltage that still ensures tolerable performance.



FIG. 5 is a flow chart that schematically illustrates yet another method for estimating and correcting BPD effects, in accordance with an embodiment of the present invention. As explained above, over-programmed memory cells are a major source of BPD distortion. The method of FIG. 5 detects the presence of over-programmed cells and reduces the BPD effects they produce.


The method begins with the MSP identifying over-programmed memory cells by reading the threshold voltages of cells in array 44, at an over-programmed cell identification step 110. Detection of over-programmed cells may be performed at any time during the memory device's lifetime. For example, the MSP may detect over-programmed cells immediately after programming, such as by using the last verification iteration of the P&V process that programs the cells. Additionally or alternatively, the MSP may detect over-programmed cells during read operations. Further additionally or alternatively, the MSP may run a background task that identifies over-programmed cells during idle periods of system 40. Identification of over-programmed cells can be performed periodically for all cells, after each memory access operation, in response to a certain event (e.g., failure of an ECC or other error detection code) or using any other suitable logic.


The MSP stores over-programming information regarding the identified over-programmed cells, at an over-programming information storage step 114. The MSP may collect and store any suitable information regarding the over-programmed cells identified at step 110, such as the locations of the over-programmed cells, quantitative measures indicative of the amount or severity of over-programming, information pertaining to groups of cells (e.g., the number of over-programmed cells in a given NAND string or erasure block) and/or any other suitable information.


The information can be stored in any suitable location, such as in some of the memory cells of array 44 or in a different memory. In some embodiments, the MSP compresses the information regarding over-programmed cells before storage, in order to reduce the amount of memory needed for storing the information.


When reading data from the memory cells in the array, the MSP processes the stored over-programming information so as to reduce the BPD-related distortion in the read cells, at a BPD reduction step 118. For example, when reading a set of potentially-interfered cells, the MSP may estimate the BPD distortion caused to the potentially-interfered cells by the over-programmed cells, and subtract the distortion from the threshold voltages read from the potentially-interfered cells.


In some embodiments, the ECC decoding process used by the MSP operates on quality metrics associated with the values read from the cells. In these embodiments, the MSP may calculate the quality metrics of the potentially-interfered cells based on the over-programming information. For example, the ECC decoding process may accept erasures, which indicate values of poor quality. The MSP may identify that some of the potentially-interfered cells have poor reliability because of BPD, and mark the values read from these cells as “erasures” to the ECC decoding process.


As yet another example, the ECC decoding process may operate on soft quality metrics, such as on Log Likelihood Ratios (LLRs). The MSP can estimate statistical characteristics of the BPD distortion, and calculate the soft quality metrics of data values of the potentially-interfered cells based on the BPD statistics. Additionally or alternatively, the MSP may use any other suitable method for reducing the BPD distortion in the potentially-interfered cells using the stored information regarding over-programmed cells. Some aspects of using quality metrics for decoding ECC in memory arrays are described in PCT Application WO 2007/132457, entitled “Combined Distortion Estimation and Error Correction Coding for Memory Devices, filed May 10, 2007, whose disclosure is incorporated herein by reference.


In some embodiments, the MSP may search for potentially-interfering cells (which may or may not be over-programmed), read the threshold voltages of the potentially-interfering cells and use the read threshold voltages for reducing or canceling the BPD distortion in potentially-interfered cells. For example, as explained in the description of FIG. 3 above, when ECC decoding of a page in word line WLn fails, the MSP may read the potentially-interfering cells in word lines WLn+1, WLn+2, . . . , WLN, wherein N denotes the number of word lines in the block. The MSP can then estimate the BPD error caused to each potentially-interfered cell in WLn based on the values read from the potentially-interfering cells, subtract the estimated BPD error from the values read from the potentially-interfered cells of WLn, and re-apply ECC decoding.


The BPD reduction techniques used for implementing step 118 can also be used in conjunction with other BPD identification methods, such as when BPD is estimated using the methods of FIG. 3 above.



FIG. 6 is a flow chart that schematically illustrates another method for estimating and correcting BPD effects, in accordance with another embodiment of the present invention. In this method, the MSP measures shifts in the threshold voltages of memory cells, and estimates the BPD effect that may be caused by these shifts on potentially-interfered cells.


The method begins with the MSP estimating the threshold voltage shifts in the memory cells of a given NAND string, at a shift estimation step 120. (Although the method description refers to a certain NAND string, the method is typically carried out during normal read operations performed on the memory, and is performed for multiple NAND strings in parallel.)


The MSP estimates the threshold voltage shifts that occurred since the cells were programmed, e.g., due to aging. For example, the MSP may apply ECC decoding so as to detect the programming level of each cell, and compare the threshold voltage read from the cell to the nominal threshold value associated with the cell's programming level. Alternatively, the MSP can estimate the threshold voltage shifts using any other suitable technique.


The MSP then estimates the BPD distortion that may be caused by the threshold voltage shifts to potentially-interfered cells, at a BPD estimation step 124. Typically, the MSP assumes that the threshold voltage shifts of a cell in word line WLn may cause BPD primarily in the cells that are located in word lines WLn+1, Wn+2, . . . of the same NAND string. The MSP then corrects the estimated BPD distortion in the potentially-interfered cells, at a BPD correction step 128. The MSP may correct the BPD distortion using any of the techniques described above, such as by directly subtracting the BPD errors from the values read from the potentially-interfered cells, or by calculating quality metrics of the potentially-interfered cells based on the estimated BPD error.


Since the methods described herein reduce BPD distortion effects, the MSP may reduce the pass voltages (VPASS) that are applied to the unselected word lines of the NAND string when using any of these methods. Thus, the methods described herein enable reducing the memory device power dissipation, program disturbs and/or cell wearing. Alternatively, the BPD cancellation methods described herein can enable increasing the voltage levels of the cells, allowing a higher voltage window, more reliable operation and/or a higher number of programming levels (i.e., more information bits per cell).


Although the embodiments described herein mainly address reducing BPD error in NAND strings, the methods and systems described herein can also be used for reducing distortion in any other suitable group of memory cells. Although the embodiments described herein mainly address writing and reading data in solid-state memory devices, the principles of the present invention can also be used for programming and reading other types of storage devices, such as Hard Disk Drives (HDD).


It will thus be appreciated that the embodiments described above are cited by way of example, and that the present invention is not limited to what has been particularly shown and described hereinabove. Rather, the scope of the present invention includes both combinations and sub-combinations of the various features described hereinabove, as well as variations and modifications thereof which would occur to persons skilled in the art upon reading the foregoing description and which are not disclosed in the prior art.

Claims
  • 1. A method for operating a memory that includes multiple analog memory cells, the method comprising: storing data in the memory by writing first storage values to the cells, so as to cause the cells to hold respective electrical charge levels;after storing the data, reading second storage values from at least some of the cells, including at least one interfered cell that belongs to a group of cells;detecting and canceling a Back Pattern Dependency (BPD) distortion caused by the electrical charge levels of one or more interfering cells in the group to at least one of the second storage values read from the at least one interfered cell; andprocessing the second storage values, including the at least one of the second storage values in which the BPD distortion was canceled, so as to reconstruct the data,wherein detecting and canceling the BPD distortion comprise recursively estimating a part of the BPD distortion in a given cell in the group based on one or more other parts of the BPD distortion in respective one or more cells in the group that were programmed earlier than the given cell, and on the second storage values that were read from the one or more cells,and wherein estimating the part of the BPD distortion comprises measuring a total BPD distortion that is caused by the cells in the group, and estimating the part of the BPD distortion based on the total BPD distortion.
  • 2. The method according to claim 1, wherein the analog memory cells comprise NAND Flash cells, and wherein the group of cells comprises a NAND string.
  • 3. The method according to claim 1, wherein detecting and canceling the BPD distortion comprise evaluating a condition indicating that the interfered cell is likely to be subject to the BPD distortion, and canceling the BPD distortion responsively to the evaluated condition.
  • 4. The method according to claim 3, wherein storing the data comprises encoding the data with an error detection code, and wherein evaluating the condition comprises decoding the error detection code and identifying a data error using the decoded error detection code.
  • 5. The method according to claim 3, wherein reading the second storage values comprises processing the read second storage values so as to identify one or more over-programmed cells among the memory cells in the memory, and wherein evaluating the condition comprises determining that the group of cells comprises at least one of the over-programmed cells.
  • 6. The method according to claim 1, wherein the cells in the group are connected in series to one another, and wherein measuring the total BPD distortion comprises applying pass voltages to respective gates of the cells in the group and measuring a current flowing through the cells responsively to the applied pass voltages.
  • 7. The method according to claim 1, wherein the cells in the group are connected in series to one another, wherein reading the second storage value from the interfered cell comprises applying a read voltage to a gate of the interfered cell, applying pass voltages to respective gates of the other cells in the group and measuring a current flowing through the cells responsively to the applied read voltage and pass voltages, and wherein canceling the BPD distortion comprises increasing at least one of the pass voltages and reading the second storage value from the interfered cell using the increased pass voltages.
  • 8. The method according to claim 7, wherein increasing the pass voltages comprises increasing the pass voltages of only a subset of the cells in the group that were programmed later than the interfered cell.
  • 9. The method according to claim 7, wherein increasing the pass voltages comprises increasing the pass voltages iteratively until meeting a condition indicating that the BPD distortion is canceled.
  • 10. The method according to claim 9, wherein increasing the pass voltages comprises storing the pass voltages for which the condition was met, and applying the stored pass voltages when performing a subsequent read operation on the group of cells.
  • 11. The method according to claim 7, and comprising, after reading the second storage value from the interfered cell using the increased pass voltages, copying the data stored in the group to another group of the memory cells.
  • 12. The method according to claim 7, wherein reading the second storage value from the interfered cell comprises reading the second storage value multiple times while applying respective different values of the pass voltages to the gates of the other cells in the group, and wherein canceling the BPD distortion comprises selecting one of the read second storage values having a lowest level of the BPD distortion.
  • 13. The method according to claim 1, wherein detecting the BPD distortion comprises measuring shifts in one or more of the second storage values read from a subset of the cells in the group, which occurred after the cells were programmed, and estimating the BPD distortion responsively to the measured shifts.
  • 14. The method according to claim 1, wherein storing the data comprises encoding the data with an Error Correction Code (ECC), wherein canceling the BPD distortion comprises computing metrics for decoding the ECC based on the detected BPD distortion, and wherein processing the second storage values so as to reconstruct the data comprises decoding the ECC responsively to the metrics.
  • 15. The method according to claim 1, wherein reading the second storage values comprises processing the read second storage values so as to identify one or more over-programmed cells among the memory cells in the memory and storing information regarding the identified over-programmed cells, and wherein canceling the BPD distortion comprises processing the stored information regarding the over-programmed cells with regard to the read second storage values so as to cancel the BPD distortion.
  • 16. The method according to claim 15, wherein processing the stored information comprises estimating the BPD distortion based on the stored information and subtracting the estimated BPD distortion from the second storage value read from the interfered cell.
  • 17. The method according to claim 15, wherein storing the data comprises encoding the data with an Error Correction Code (ECC), wherein processing the stored information comprises calculating metrics for decoding the ECC based on the stored information, and wherein processing the second storage values so as to reconstruct the data comprises decoding the ECC responsively to the metrics.
  • 18. A method for operating a memory that includes multiple analog memory cells, the method comprising: storing data in the memory by writing first storage values to the cells, so as to cause the cells to hold respective electrical charge levels;after storing the data, reading second storage values from at least some of the cells, including at least one interfered cell that belongs to a group of cells;detecting and canceling a Back Pattern Dependency (BPD) distortion caused by the electrical charge levels of one or more interfering cells in the group to at least one of the second storage values read from the at least one interfered cell; andprocessing the second storage values, including the at least one of the second storage values in which the BPD distortion was canceled, so as to reconstruct the data,wherein the cells in the group are connected in series to one another, wherein reading the second storage value from the interfered cell comprises applying a read voltage to a gate of the interfered cell, applying pass voltages to respective gates of the other cells in the group and measuring a current flowing through the cells responsively to the applied read voltage and pass voltages, and wherein canceling the BPD distortion comprises reducing at least one of the pass voltages while not violating a predefined performance criterion, and reading the second storage value from the interfered cell using the reduced pass voltages.
  • 19. A data storage apparatus, comprising: an interface, which is operative to communicate with a memory that includes multiple analog memory cells; anda processor, which is coupled to store data in the memory by writing first storage values to the cells so as to cause the cells to hold respective electrical charge levels, to read, after storing the data, second storage values from at least some of the cells, including at least one interfered cell that belongs to a group of cells, to detect and cancel a Back Pattern Dependency (BPD) distortion caused by the electrical charge levels of one or more interfering cells in the group to at least one of the second storage values read from the at least one interfered cell, and to process the second storage values, including the at least one of the second storage values in which the BPD distortion was canceled, so as to reconstruct the data,wherein the processor is coupled to recursively estimate a part of the BPD distortion in a given cell in the group based on one or more other parts of the BPD distortion in one or more cells in the group that were programmed earlier than the given cell, and on the second storage values that were read from the one or more cells, and to estimate the part of the BPD distortion by measuring a total BPD distortion that is caused by the cells in the group, and estimating the part of the BPD distortion based on the total BPD distortion.
  • 20. The apparatus according to claim 19, wherein the analog memory cells comprise NAND Flash cells, and wherein the group of cells comprises a NAND string.
  • 21. The apparatus according to claim 19, wherein the processor is coupled to evaluate a condition indicating that the interfered cell is likely to be subject to the BPD distortion, and to cancel the BPD distortion responsively to the evaluated condition.
  • 22. The apparatus according to claim 21, wherein the processor is coupled to encode the data with an error detection code and to decode the error detection code when reconstructing the data, and wherein the condition checks whether a data error is identified by the decoded error detection code.
  • 23. The apparatus according to claim 21, wherein the processor is coupled to process the read second storage values so as to identify one or more over-programmed cells among the memory cells in the memory, and wherein the condition checks whether the group of cells comprises at least one of the over-programmed cells.
  • 24. The apparatus according to claim 19, wherein the cells in the group are connected in series to one another, and wherein the processor is coupled to measure the total BPD distortion by applying pass voltages to respective gates of the cells in the group and measuring a current flowing through the cells responsively to the applied pass voltages.
  • 25. The apparatus according to claim 19, wherein the cells in the group are connected in series to one another, wherein the processor is coupled to read the second storage value from the interfered cell by applying a read voltage to a gate of the interfered cell, applying pass voltages to respective gates of the other cells in the group and measuring a current flowing through the cells responsively to the applied read voltage and pass voltages, and to cancel the BPD distortion by increasing at least one of the pass voltages and reading the second storage value from the interfered cell using the increased pass voltages.
  • 26. The apparatus according to claim 25, wherein the processor is coupled to increase the pass voltages of only a subset of the cells in the group that were programmed later than the interfered cell.
  • 27. The apparatus according to claim 25, wherein the processor is coupled to increase the pass voltages iteratively until meeting a condition indicating that the BPD distortion is canceled.
  • 28. The apparatus according to claim 27, wherein the processor is coupled to store the pass voltages for which the condition was met, and to apply the stored pass voltages when performing a subsequent read operation on the group of cells.
  • 29. The apparatus according to claim 25, wherein the processor is coupled to copy the data stored in the group to another group of the memory cells after reading the second storage value from the interfered cell using the increased pass voltages.
  • 30. The apparatus according to claim 25, wherein the processor is coupled to read the second storage value from the interfered cell multiple times while applying respective different values of the pass voltages to the gates of the other cells in the group, and to select one of the read second storage values having a lowest level of the BPD distortion.
  • 31. The apparatus according to claim 19, wherein the processor is coupled to process the read second storage values so as to identify one or more over-programmed cells among the memory cells in the memory, to store information regarding the identified over-programmed cells, and to process the stored information regarding the over-programmed cells with regard to the read second storage values so as to cancel the BPD distortion.
  • 32. The apparatus according to claim 31, wherein the processor is coupled to estimate the BPD distortion based on the stored information and to subtract the estimated BPD distortion from the second storage value read from the interfered cell.
  • 33. The apparatus according to claim 31, wherein the processor is coupled to encode the data with an Error Correction Code (ECC), to calculate metrics for decoding the ECC based on the stored information, and to decode the ECC responsively to the metrics.
  • 34. The apparatus according to claim 19, wherein the processor is coupled to measure shifts in one or more of the second storage values read from a subset of the cells in the group, which occurred after the cells were programmed, and to estimate the BPD distortion responsively to the measured shifts.
  • 35. The apparatus according to claim 19, wherein the processor is coupled to encode the data with an Error Correction Code (ECC), to compute metrics for decoding the ECC based on the detected BPD distortion, and to decode the ECC responsively to the metrics so as to reconstruct the data.
  • 36. A data storage apparatus, comprising: an interface, which is operative to communicate with a memory that includes multiple analog memory cells; anda processor, which is coupled to store data in the memory by writing first storage values to the cells so as to cause the cells to hold respective electrical charge levels, to read, after storing the data, second storage values from at least some of the cells, including at least one interfered cell that belongs to a group of cells, to detect and cancel a Back Pattern Dependency (BPD) distortion caused by the electrical charge levels of one or more interfering cells in the group to at least one of the second storage values read from the at least one interfered cell, and to process the second storage values, including the at least one of the second storage values in which the BPD distortion was canceled, so as to reconstruct the data,wherein the cells in the group are connected in series to one another, wherein the processor is coupled to read the second storage value from the interfered cell by applying a read voltage to a gate of the interfered cell, applying pass voltages to respective gates of the other cells in the group and measuring a current flowing through the cells responsively to the applied read voltage and pass voltages, and to cancel the BPD distortion by reducing at least one of the pass voltages while not violating a predefined performance criterion, and reading the second storage value from the interfered cell using the reduced pass voltages.
  • 37. A data storage apparatus, comprising: a memory, which comprises multiple analog memory cells; anda processor, which is coupled to store data in the memory by writing first storage values to the cells so as to cause the cells to hold respective electrical charge levels, to read, after storing the data, second storage values from at least some of the cells, including at least one interfered cell that belongs to a group of cells, to detect and cancel a Back Pattern Dependency (BPD) distortion caused by the electrical charge levels of one or more interfering cells in the group to at least one of the second storage values read from the at least one interfered cell, and to process the second storage values, including the at least one of the second storage values in which the BPD distortion was canceled, so as to reconstruct the data,wherein the processor is coupled to recursively estimate a part of the BPD distortion in a given cell in the group based on one or more other parts of the BPD distortion in one or more cells in the group that were programmed earlier than the given cell, and on the second storage values that were read from the one or more cells, and to estimate the part of the BPD distortion by measuring a total BPD distortion that is caused by the cells in the group, and estimating the part of the BPD distortion based on the total BPD distortion.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation in part of U.S. patent application Ser. No. 11/995,801, filed Jan. 15, 2008, which is a national phase application of PCT Application WO 2007/132453 claiming priority from U.S. Provisional Patent Application No. 60/886,429, filed Jan. 24, 2007, whose disclosures are all incorporated herein by reference. This application claims the benefit of U.S. Provisional Patent Application No. 60/891,569, filed Feb. 26, 2007 and U.S. Provisional Patent Application No. 60/938,192, filed May 16, 2007, whose disclosures are incorporated herein by reference.

US Referenced Citations (537)
Number Name Date Kind
3668631 Griffith et al. Jun 1972 A
3668632 Oldham Jun 1972 A
4058851 Scheuneman Nov 1977 A
4112502 Scheuneman Sep 1978 A
4394763 Nagano et al. Jul 1983 A
4413339 Riggle et al. Nov 1983 A
4556961 Iwahashi et al. Dec 1985 A
4558431 Satoh Dec 1985 A
4608687 Dutton Aug 1986 A
4654847 Dutton Mar 1987 A
4661929 Aoki et al. Apr 1987 A
4768171 Tada Aug 1988 A
4811285 Walker et al. Mar 1989 A
4899342 Potter et al. Feb 1990 A
4910706 Hyatt Mar 1990 A
4993029 Galbraith et al. Feb 1991 A
5056089 Furuta et al. Oct 1991 A
5077722 Geist et al. Dec 1991 A
5126808 Montalvo et al. Jun 1992 A
5163021 Mehrotra et al. Nov 1992 A
5172338 Mehrotta et al. Dec 1992 A
5182558 Mayo Jan 1993 A
5182752 DeRoo et al. Jan 1993 A
5191584 Anderson Mar 1993 A
5200959 Gross et al. Apr 1993 A
5237535 Mielke et al. Aug 1993 A
5272669 Samachisa et al. Dec 1993 A
5276649 Hoshita et al. Jan 1994 A
5287469 Tsuboi Feb 1994 A
5365484 Cleveland et al. Nov 1994 A
5388064 Khan Feb 1995 A
5416646 Shirai May 1995 A
5416782 Wells et al. May 1995 A
5446854 Khalidi et al. Aug 1995 A
5450424 Okugaki et al. Sep 1995 A
5469444 Endoh et al. Nov 1995 A
5473753 Wells et al. Dec 1995 A
5479170 Cauwenberghs et al. Dec 1995 A
5508958 Fazio et al. Apr 1996 A
5519831 Holzhammer May 1996 A
5532962 Auclair et al. Jul 1996 A
5541886 Hasbun Jul 1996 A
5600677 Citta et al. Feb 1997 A
5638320 Wong et al. Jun 1997 A
5657332 Auclair et al. Aug 1997 A
5675540 Roohparvar Oct 1997 A
5682352 Wong et al. Oct 1997 A
5687114 Khan Nov 1997 A
5696717 Koh Dec 1997 A
5726649 Tamaru et al. Mar 1998 A
5726934 Tran et al. Mar 1998 A
5742752 De Koning Apr 1998 A
5748533 Dunlap et al. May 1998 A
5748534 Dunlap et al. May 1998 A
5751637 Chen et al. May 1998 A
5761402 Kaneda et al. Jun 1998 A
5798966 Keeney Aug 1998 A
5799200 Brant et al. Aug 1998 A
5801985 Roohparvar et al. Sep 1998 A
5838832 Barnsley Nov 1998 A
5860106 Domen et al. Jan 1999 A
5867114 Barbir Feb 1999 A
5867428 Ishii et al. Feb 1999 A
5867429 Chen et al. Feb 1999 A
5877986 Harari et al. Mar 1999 A
5889937 Tamagawa Mar 1999 A
5901089 Korsh et al. May 1999 A
5909449 So et al. Jun 1999 A
5912906 Wu et al. Jun 1999 A
5930167 Lee et al. Jul 1999 A
5937424 Leak et al. Aug 1999 A
5942004 Cappelletti Aug 1999 A
5946716 Karp et al. Aug 1999 A
5969986 Wong et al. Oct 1999 A
5982668 Ishii et al. Nov 1999 A
5991517 Harari et al. Nov 1999 A
5995417 Chen et al. Nov 1999 A
6009014 Hollmer et al. Dec 1999 A
6009016 Ishii et al. Dec 1999 A
6023425 Ishii et al. Feb 2000 A
6034891 Norman Mar 2000 A
6040993 Chen et al. Mar 2000 A
6041430 Yamauchi Mar 2000 A
6073204 Lakhani et al. Jun 2000 A
6101614 Gonzales et al. Aug 2000 A
6128237 Shirley et al. Oct 2000 A
6134140 Tanaka et al. Oct 2000 A
6134143 Norman Oct 2000 A
6134631 Jennings Oct 2000 A
6141261 Patti Oct 2000 A
6151246 So et al. Nov 2000 A
6157573 Ishii et al. Dec 2000 A
6166962 Chen et al. Dec 2000 A
6169691 Pasotti et al. Jan 2001 B1
6178466 Gilbertson et al. Jan 2001 B1
6185134 Tanaka et al. Feb 2001 B1
6209113 Roohparvar Mar 2001 B1
6212654 Lou et al. Apr 2001 B1
6219276 Parker Apr 2001 B1
6219447 Lee et al. Apr 2001 B1
6222762 Guterman et al. Apr 2001 B1
6230233 Lofgren et al. May 2001 B1
6240458 Gilbertson May 2001 B1
6259627 Wong Jul 2001 B1
6275419 Guterman et al. Aug 2001 B1
6278632 Chevallier Aug 2001 B1
6279069 Robinson et al. Aug 2001 B1
6288944 Kawamura Sep 2001 B1
6292394 Cohen et al. Sep 2001 B1
6301151 Engh et al. Oct 2001 B1
6304486 Yano Oct 2001 B1
6307776 So et al. Oct 2001 B1
6804805 Rub Oct 2001 B2
6317363 Guterman et al. Nov 2001 B1
6317364 Guterman et al. Nov 2001 B1
6345004 Omura et al. Feb 2002 B1
6360346 Miyauchi et al. Mar 2002 B1
6363008 Wong Mar 2002 B1
6363454 Lakhani et al. Mar 2002 B1
6366496 Torelli et al. Apr 2002 B1
6385092 Ishii et al. May 2002 B1
6392932 Ishii et al. May 2002 B1
6396742 Korsh et al. May 2002 B1
6397364 Barkan May 2002 B1
6405323 Lin et al. Jun 2002 B1
6405342 Lee Jun 2002 B1
6418060 Yong et al. Jul 2002 B1
6442585 Dean et al. Aug 2002 B1
6445602 Kokudo et al. Sep 2002 B1
6452838 Ishii et al. Sep 2002 B1
6456528 Chen Sep 2002 B1
6466476 Wong et al. Oct 2002 B1
6467062 Barkan Oct 2002 B1
6469931 Ban et al. Oct 2002 B1
6490236 Fukuda et al. Dec 2002 B1
6522580 Chen et al. Feb 2003 B2
6525952 Araki et al. Feb 2003 B2
6532556 Wong et al. Mar 2003 B1
6538922 Khalid et al. Mar 2003 B1
6549464 Tanaka et al. Apr 2003 B2
6553510 Pekny Apr 2003 B1
6558967 Wong May 2003 B1
6560152 Cernea May 2003 B1
6567311 Ishii et al. May 2003 B2
6577539 Iwahashi Jun 2003 B2
6584012 Banks Jun 2003 B2
6615307 Roohparvar Sep 2003 B1
6621739 Gonzales et al. Sep 2003 B2
6640326 Buckingham et al. Oct 2003 B1
6643169 Rudelic et al. Nov 2003 B2
6646913 Micheloni et al. Nov 2003 B2
6678192 Gongwer et al. Jan 2004 B2
6683811 Ishii et al. Jan 2004 B2
6687155 Nagasue Feb 2004 B2
6707748 Lin et al. Mar 2004 B2
6708257 Bao Mar 2004 B2
6714449 Khalid Mar 2004 B2
6717847 Chen Apr 2004 B2
6731557 Beretta May 2004 B2
6738293 Iwahashi May 2004 B1
6751766 Guterman et al. Jun 2004 B2
6757193 Chen et al. Jun 2004 B2
6774808 Hibbs et al. Aug 2004 B1
6781877 Cernea et al. Aug 2004 B2
6807095 Chen et al. Oct 2004 B2
6807101 Ooishi et al. Oct 2004 B2
6809964 Moschopoulos et al. Oct 2004 B2
6819592 Noguchi et al. Nov 2004 B2
6829167 Tu et al. Dec 2004 B2
6845052 Ho et al. Jan 2005 B1
6851018 Wyatt et al. Feb 2005 B2
6851081 Yamamoto Feb 2005 B2
6856546 Guterman et al. Feb 2005 B2
6862218 Guterman et al. Mar 2005 B2
6870767 Rudelic et al. Mar 2005 B2
6870773 Noguchi et al. Mar 2005 B2
6873552 Ishii et al. Mar 2005 B2
6879520 Hosono et al. Apr 2005 B2
6882567 Wong Apr 2005 B1
6894926 Guterman et al. May 2005 B2
6907497 Hosono et al. Jun 2005 B2
6925009 Noguchi et al. Aug 2005 B2
6930925 Guo et al. Aug 2005 B2
6934188 Roohparvar Aug 2005 B2
6937511 Hsu et al. Aug 2005 B2
6958938 Noguchi et al. Oct 2005 B2
6963505 Cohen Nov 2005 B2
6972993 Conley et al. Dec 2005 B2
6988175 Lasser Jan 2006 B2
6992932 Cohen Jan 2006 B2
6999344 Hosono et al. Feb 2006 B2
7002843 Guterman et al. Feb 2006 B2
7006379 Noguchi et al. Feb 2006 B2
7012835 Gonzales et al. Mar 2006 B2
7020017 Chen et al. Mar 2006 B2
7023735 Ban et al. Apr 2006 B2
7031210 Park et al. Apr 2006 B2
7031214 Tran Apr 2006 B2
7031216 You Apr 2006 B2
7039846 Hewitt et al. May 2006 B2
7042766 Wang et al. May 2006 B1
7054193 Wong May 2006 B1
7054199 Lee et al. May 2006 B2
7057958 So et al. Jun 2006 B2
7065147 Ophir et al. Jun 2006 B2
7068539 Guterman et al. Jun 2006 B2
7071849 Zhang Jul 2006 B2
7072222 Ishii et al. Jul 2006 B2
7079555 Baydar et al. Jul 2006 B2
7088615 Guterman et al. Aug 2006 B2
7099194 Tu et al. Aug 2006 B2
7102924 Chen et al. Sep 2006 B2
7113432 Mokhlesi Sep 2006 B2
7130210 Bathul et al. Oct 2006 B2
7139192 Wong Nov 2006 B1
7139198 Guterman et al. Nov 2006 B2
7145805 Ishii et al. Dec 2006 B2
7151692 Wu Dec 2006 B2
7170781 So et al. Jan 2007 B2
7170802 Cernea et al. Jan 2007 B2
7173859 Hemink Feb 2007 B2
7177184 Chen Feb 2007 B2
7177195 Gonzales et al. Feb 2007 B2
7177199 Chen et al. Feb 2007 B2
7177200 Ronen et al. Feb 2007 B2
7184338 Nagakawa et al. Feb 2007 B2
7187195 Kim Mar 2007 B2
7187592 Guterman et al. Mar 2007 B2
7190614 Wu Mar 2007 B2
7193898 Cernea Mar 2007 B2
7193921 Choi et al. Mar 2007 B2
7196644 Anderson et al. Mar 2007 B1
7196928 Chen Mar 2007 B2
7196933 Shibata Mar 2007 B2
7197594 Raz et al. Mar 2007 B2
7200062 Kinsely et al. Apr 2007 B2
7210077 Brandenberger et al. Apr 2007 B2
7221592 Nazarian May 2007 B2
7224613 Chen et al. May 2007 B2
7231474 Helms et al. Jun 2007 B1
7231562 Ohlhoff et al. Jun 2007 B2
7243275 Gongwer et al. Jul 2007 B2
7254690 Rao Aug 2007 B2
7254763 Aadsen et al. Aug 2007 B2
7257027 Park Aug 2007 B2
7259987 Chen et al. Aug 2007 B2
7266026 Gongwer et al. Sep 2007 B2
7266069 Chu Sep 2007 B2
7269066 Nguyen et al. Sep 2007 B2
7272757 Stocken Sep 2007 B2
7274611 Roohparvar Sep 2007 B2
7277355 Tanzawa Oct 2007 B2
7280398 Lee et al. Oct 2007 B1
7280409 Misumi et al. Oct 2007 B2
7280415 Hwang et al. Oct 2007 B2
7283399 Ishii et al. Oct 2007 B2
7289344 Chen Oct 2007 B2
7301807 Khalid et al. Nov 2007 B2
7301817 Li et al. Nov 2007 B2
7308525 Lasser et al. Dec 2007 B2
7310255 Chan Dec 2007 B2
7310269 Shibata Dec 2007 B2
7310271 Lee Dec 2007 B2
7310272 Mokhlesi et al. Dec 2007 B1
7310347 Lasser Dec 2007 B2
7321509 Chen et al. Jan 2008 B2
7328384 Kulkarni et al. Feb 2008 B1
7342831 Mokhlesi et al. Mar 2008 B2
7343330 Boesjes et al. Mar 2008 B1
7345924 Nguyen et al. Mar 2008 B2
7345928 Li Mar 2008 B2
7349263 Kim et al. Mar 2008 B2
7356755 Fackenthal Apr 2008 B2
7363420 Lin et al. Apr 2008 B2
7365671 Anderson Apr 2008 B1
7388781 Litsyn et al. Jun 2008 B2
7397697 So et al. Jul 2008 B2
7405974 Yaoi et al. Jul 2008 B2
7405979 Ishii et al. Jul 2008 B2
7408804 Hemink et al. Aug 2008 B2
7408810 Aritome et al. Aug 2008 B2
7409473 Conley et al. Aug 2008 B2
7409623 Baker et al. Aug 2008 B2
7420847 Li Sep 2008 B2
7433231 Aritome Oct 2008 B2
7433697 Karaoguz et al. Oct 2008 B2
7434111 Sugiura et al. Oct 2008 B2
7437498 Ronen Oct 2008 B2
7440324 Mokhlesi Oct 2008 B2
7440331 Hemink Oct 2008 B2
7441067 Gorobetz et al. Oct 2008 B2
7447970 Wu et al. Nov 2008 B2
7450421 Mokhlesi et al. Nov 2008 B2
7453737 Ha Nov 2008 B2
7457163 Hemink Nov 2008 B2
7457897 Lee et al. Nov 2008 B1
7460410 Nagai et al. Dec 2008 B2
7460412 Lee et al. Dec 2008 B2
7466592 Mitani et al. Dec 2008 B2
7468907 Kang et al. Dec 2008 B2
7468911 Lutze et al. Dec 2008 B2
7471581 Tran et al. Dec 2008 B2
7483319 Brown Jan 2009 B2
7487329 Hepkin et al. Feb 2009 B2
7492641 Hosono et al. Feb 2009 B2
7508710 Mokhlesi Mar 2009 B2
7526711 Orio Apr 2009 B2
7539061 Lee May 2009 B2
7539062 Doyle May 2009 B2
7551492 Kim Jun 2009 B2
7558109 Brandman et al. Jul 2009 B2
7558839 McGovern Jul 2009 B1
7568135 Cornwell et al. Jul 2009 B2
7570520 Kamei et al. Aug 2009 B2
7590002 Mokhlesi et al. Sep 2009 B2
7593259 Kim et al. Sep 2009 B2
7594093 Kancherla Sep 2009 B1
7596707 Vemula Sep 2009 B1
7609787 Jahan et al. Oct 2009 B2
7613043 Cornwell et al. Nov 2009 B2
7616498 Mokhlesi et al. Nov 2009 B2
7619918 Aritome Nov 2009 B2
7631245 Lasser Dec 2009 B2
7633798 Sarin et al. Dec 2009 B2
7633802 Mokhlesi Dec 2009 B2
7639532 Roohparvar et al. Dec 2009 B2
7644347 Alexander et al. Jan 2010 B2
7656734 Thorp et al. Feb 2010 B2
7660158 Aritome Feb 2010 B2
7660183 Ware et al. Feb 2010 B2
7661054 Huffman et al. Feb 2010 B2
7665007 Yang et al. Feb 2010 B2
7680987 Clark et al. Mar 2010 B1
7733712 Walston et al. Jun 2010 B1
7742351 Inoue et al. Jun 2010 B2
7761624 Karamcheti et al. Jul 2010 B2
7810017 Radke Oct 2010 B2
7848149 Gonzales et al. Dec 2010 B2
7869273 Lee et al. Jan 2011 B2
7885119 Li Feb 2011 B2
7928497 Yaegashi Apr 2011 B2
7930515 Gupta et al. Apr 2011 B2
7945825 Cohen et al. May 2011 B2
7978516 Olbrich et al. Jul 2011 B2
8014094 Jin Sep 2011 B1
8037380 Cagno et al. Oct 2011 B2
8040744 Gorobets et al. Oct 2011 B2
20010002172 Tanaka et al. May 2001 A1
20010006479 Ikehashi et al. Jul 2001 A1
20020038440 Barkan Mar 2002 A1
20020056064 Kidorf et al. May 2002 A1
20020118574 Gongwer et al. Aug 2002 A1
20020133684 Anderson Sep 2002 A1
20020166091 Kidorf et al. Nov 2002 A1
20020174295 Ulrich et al. Nov 2002 A1
20020196510 Hietala et al. Dec 2002 A1
20030002348 Chen et al. Jan 2003 A1
20030103400 Van Tran Jun 2003 A1
20030161183 Van Tran Aug 2003 A1
20030189856 Cho et al. Oct 2003 A1
20040057265 Mirabel et al. Mar 2004 A1
20040057285 Cernea et al. Mar 2004 A1
20040083333 Chang et al. Apr 2004 A1
20040083334 Chang et al. Apr 2004 A1
20040105311 Cernea et al. Jun 2004 A1
20040114437 Li Jun 2004 A1
20040160842 Fukiage Aug 2004 A1
20040223371 Roohparvar Nov 2004 A1
20050007802 Gerpheide Jan 2005 A1
20050013165 Ban Jan 2005 A1
20050024941 Lasser et al. Feb 2005 A1
20050024978 Ronen Feb 2005 A1
20050030788 Parkinson et al. Feb 2005 A1
20050086574 Fackenthal Apr 2005 A1
20050121436 Kamitani et al. Jun 2005 A1
20050157555 Ono et al. Jul 2005 A1
20050162913 Chen Jul 2005 A1
20050169051 Khalid et al. Aug 2005 A1
20050189649 Maruyama et al. Sep 2005 A1
20050213393 Lasser Sep 2005 A1
20050224853 Ohkawa Oct 2005 A1
20050240745 Iyer et al. Oct 2005 A1
20050243626 Ronen Nov 2005 A1
20060004952 Lasser Jan 2006 A1
20060028875 Avraham et al. Feb 2006 A1
20060028877 Meir Feb 2006 A1
20060101193 Murin May 2006 A1
20060106972 Gorobets et al. May 2006 A1
20060107136 Gongwer et al. May 2006 A1
20060129750 Lee et al. Jun 2006 A1
20060133141 Gorobets Jun 2006 A1
20060156189 Tomlin Jul 2006 A1
20060179334 Brittain et al. Aug 2006 A1
20060190699 Lee Aug 2006 A1
20060203546 Lasser Sep 2006 A1
20060218359 Sanders et al. Sep 2006 A1
20060221692 Chen Oct 2006 A1
20060221705 Hemink et al. Oct 2006 A1
20060221714 Li et al. Oct 2006 A1
20060239077 Park et al. Oct 2006 A1
20060239081 Roohparvar Oct 2006 A1
20060256620 Nguyen et al. Nov 2006 A1
20060256626 Werner et al. Nov 2006 A1
20060256891 Yuan et al. Nov 2006 A1
20060271748 Jain et al. Nov 2006 A1
20060285392 Incarnati et al. Dec 2006 A1
20060285396 Ha Dec 2006 A1
20070006013 Moshayedi et al. Jan 2007 A1
20070019481 Park Jan 2007 A1
20070033581 Tomlin et al. Feb 2007 A1
20070047314 Goda et al. Mar 2007 A1
20070047326 Nguyen et al. Mar 2007 A1
20070050536 Kolokowsky Mar 2007 A1
20070058446 Hwang et al. Mar 2007 A1
20070061502 Lasser et al. Mar 2007 A1
20070067667 Ikeuchi et al. Mar 2007 A1
20070074093 Lasser Mar 2007 A1
20070086239 Litsyn et al. Apr 2007 A1
20070086260 Sinclair Apr 2007 A1
20070089034 Litsyn et al. Apr 2007 A1
20070091677 Lasser et al. Apr 2007 A1
20070091694 Lee et al. Apr 2007 A1
20070103978 Conley et al. May 2007 A1
20070103986 Chen May 2007 A1
20070109845 Chen May 2007 A1
20070109849 Chen May 2007 A1
20070115726 Cohen et al. May 2007 A1
20070118713 Guterman et al. May 2007 A1
20070143378 Gorobets Jun 2007 A1
20070143531 Atri Jun 2007 A1
20070159889 Kang et al. Jul 2007 A1
20070159892 Kang et al. Jul 2007 A1
20070159907 Kwak Jul 2007 A1
20070168837 Murin Jul 2007 A1
20070171714 Wu et al. Jul 2007 A1
20070183210 Choi et al. Aug 2007 A1
20070189073 Aritome Aug 2007 A1
20070195602 Fong et al. Aug 2007 A1
20070206426 Mokhlesi Sep 2007 A1
20070208904 Hsieh et al. Sep 2007 A1
20070226599 Motwani Sep 2007 A1
20070236990 Aritome Oct 2007 A1
20070253249 Kang et al. Nov 2007 A1
20070256620 Viggiano et al. Nov 2007 A1
20070263455 Cornwell et al. Nov 2007 A1
20070266232 Rodgers et al. Nov 2007 A1
20070271424 Lee et al. Nov 2007 A1
20070280000 Fujiu et al. Dec 2007 A1
20070291571 Balasundaram Dec 2007 A1
20070297234 Cernea et al. Dec 2007 A1
20080010395 Mylly et al. Jan 2008 A1
20080025121 Tanzawa Jan 2008 A1
20080043535 Roohparvar Feb 2008 A1
20080049504 Kasahara et al. Feb 2008 A1
20080049506 Guterman Feb 2008 A1
20080052446 Lasser et al. Feb 2008 A1
20080055993 Lee Mar 2008 A1
20080080243 Edahiro et al. Apr 2008 A1
20080082730 Kim et al. Apr 2008 A1
20080089123 Chae et al. Apr 2008 A1
20080104309 Cheon et al. May 2008 A1
20080104312 Lasser May 2008 A1
20080109590 Jung et al. May 2008 A1
20080115017 Jacobson May 2008 A1
20080123420 Brandman et al. May 2008 A1
20080126686 Sokolov et al. May 2008 A1
20080130341 Shalvi et al. Jun 2008 A1
20080148115 Sokolov et al. Jun 2008 A1
20080151618 Sharon et al. Jun 2008 A1
20080151667 Miu et al. Jun 2008 A1
20080158958 Sokolov et al. Jul 2008 A1
20080181001 Shalvi Jul 2008 A1
20080198650 Shalvi et al. Aug 2008 A1
20080198654 Toda Aug 2008 A1
20080209116 Caulkins Aug 2008 A1
20080209304 Winarski et al. Aug 2008 A1
20080215798 Sharon et al. Sep 2008 A1
20080219050 Shalvi et al. Sep 2008 A1
20080239093 Easwar et al. Oct 2008 A1
20080239812 Abiko et al. Oct 2008 A1
20080253188 Aritome Oct 2008 A1
20080263262 Sokolov et al. Oct 2008 A1
20080263676 Mo et al. Oct 2008 A1
20080270730 Lasser et al. Oct 2008 A1
20080282106 Shalvi et al. Nov 2008 A1
20080288714 Salomon et al. Nov 2008 A1
20090013233 Radke Jan 2009 A1
20090024905 Shalvi et al. Jan 2009 A1
20090034337 Aritome Feb 2009 A1
20090043831 Antonopoulos et al. Feb 2009 A1
20090043951 Shalvi et al. Feb 2009 A1
20090049234 Oh et al. Feb 2009 A1
20090073762 Lee et al. Mar 2009 A1
20090086542 Lee et al. Apr 2009 A1
20090089484 Chu Apr 2009 A1
20090091979 Shalvi Apr 2009 A1
20090094930 Schwoerer Apr 2009 A1
20090106485 Anholt Apr 2009 A1
20090112949 Ergan et al. Apr 2009 A1
20090132755 Radke May 2009 A1
20090144600 Perlmutter et al. Jun 2009 A1
20090150894 Huang et al. Jun 2009 A1
20090157950 Selinger Jun 2009 A1
20090157964 Kasorla et al. Jun 2009 A1
20090158126 Perlmutter et al. Jun 2009 A1
20090168524 Golov et al. Jul 2009 A1
20090172257 Prins et al. Jul 2009 A1
20090172261 Prins et al. Jul 2009 A1
20090193184 Yu et al. Jul 2009 A1
20090199074 Sommer et al. Aug 2009 A1
20090204824 Lin et al. Aug 2009 A1
20090204872 Yu et al. Aug 2009 A1
20090213653 Perlmutter et al. Aug 2009 A1
20090213654 Perlmutter et al. Aug 2009 A1
20090225595 Kim Sep 2009 A1
20090265509 Klein Oct 2009 A1
20090300227 Nochimowski et al. Dec 2009 A1
20090323412 Mokhlesi et al. Dec 2009 A1
20090327608 Eschmann Dec 2009 A1
20100017650 Chin et al. Jan 2010 A1
20100034022 Dutta et al. Feb 2010 A1
20100057976 Lasser Mar 2010 A1
20100061151 Miwa et al. Mar 2010 A1
20100082883 Chen et al. Apr 2010 A1
20100083247 Kanevsky et al. Apr 2010 A1
20100110580 Takashima May 2010 A1
20100131697 Alrod et al. May 2010 A1
20100142268 Aritome Jun 2010 A1
20100142277 Yang et al. Jun 2010 A1
20100169547 Ou Jul 2010 A1
20100169743 Vogan et al. Jul 2010 A1
20100174847 Paley et al. Jul 2010 A1
20110066793 Burd Mar 2011 A1
20110075482 Shepard et al. Mar 2011 A1
20110107049 Kwon et al. May 2011 A1
20110199823 Bar-Or et al. Aug 2011 A1
20110302354 Miller Dec 2011 A1
Foreign Referenced Citations (44)
Number Date Country
0783754 Jul 1997 EP
1434236 Jun 2004 EP
1605509 Dec 2005 EP
9610256 Apr 1996 WO
9828745 Jul 1998 WO
02100112 Dec 2002 WO
03100791 Dec 2003 WO
2007046084 Apr 2007 WO
2007132452 Nov 2007 WO
2007132453 Nov 2007 WO
2007132456 Nov 2007 WO
2007132457 Nov 2007 WO
2007132457 Nov 2007 WO
2007132458 Nov 2007 WO
2007146010 Dec 2007 WO
2008026203 Mar 2008 WO
2008053472 May 2008 WO
2008053473 May 2008 WO
2008068747 Jun 2008 WO
2008077284 Jul 2008 WO
2008083131 Jul 2008 WO
2008099958 Aug 2008 WO
2008111058 Sep 2008 WO
2008124760 Oct 2008 WO
2008139441 Nov 2008 WO
2009037691 Mar 2009 WO
2009037697 Mar 2009 WO
2009038961 Mar 2009 WO
2009050703 Apr 2009 WO
2009053961 Apr 2009 WO
2009053962 Apr 2009 WO
2009053963 Apr 2009 WO
2009063450 May 2009 WO
2009072100 Jun 2009 WO
2009072101 Jun 2009 WO
2009072102 Jun 2009 WO
2009072103 Jun 2009 WO
2009072104 Jun 2009 WO
2009072105 Jun 2009 WO
2009074978 Jun 2009 WO
2009074979 Jun 2009 WO
2009078006 Jun 2009 WO
2009095902 Aug 2009 WO
2011024015 Mar 2011 WO
Related Publications (1)
Number Date Country
20080219050 A1 Sep 2008 US
Provisional Applications (3)
Number Date Country
60891569 Feb 2007 US
60938192 May 2007 US
60886429 Jan 2007 US
Continuation in Parts (1)
Number Date Country
Parent 11995801 US
Child 12037487 US