Various types of existing memory each have significant limitations. For example, Dynamic Random Access Memory (DRAM) is fast, but low density and volatile. NAND is dense and inexpensive, but slow. Magnetic RAM (MRAM) is neither dense nor fast, and is also relatively expensive.
While some solutions have sought to combine NAND and DRAM, they are combined at the package level. For example, while some packages have been created that include both a NAND and a DRAM, each of the NAND and DRAM has separate interfaces with input and output. Accordingly, such solutions retain the inefficiencies of the individual memories.
The present disclosure provides for a stacked memory combining RAM and one or more layers of NVM, such as NAND. For example, a first layer of RAM, such as DRAM, is coupled to multiple consecutive layers of NAND using direct bonding interconnect (DBI®). Serialization and overhead that exists in periphery of the NVM may be stripped to manage the data stored therein. The resulting connections between the RAM and the NVM are high bandwidth, high pincount interconnects. Interconnects between each layer of NVM are also very dense.
According to some examples, the RAM may store logic for the NVM. In other examples, a separate layer of logic for the NVM may be coupled in the stack between the RAM and the NVM. While including the NVM logic in the DRAM layer may provide for a smaller stack size as compared to including the logic in a separate layer, having a separate non-volatile logic layer frees up more of the RAM for memory.
The RAM may include an interface for receiving data for storage and outputting data from storage. The logic may determine which data is stored in NVM and which data is buffered for RAM storage. According to some examples, data to be stored in the memory stack may be tagged to indicate whether it is for short term storage in the RAM or long term storage in the NVM layers. Just as one example, machine learning may be used to detect whether the data received at an input to the DRAM is intended for short term or long term storage.
DRAM arrays can serve as registers and temporarily hold data loaded from a flash NVM plane and in turn supply this data to the outside world. Multiple registers operating together can allow for proportionally faster uninterrupted I/O speeds. For example, if four registers are used, each associated with a different flash memory array (plane), if the array page load speed is 25 us, a clock speed of 12 ns could be supported. 8 planes could support a read clock speed of 6 ns.
The RAM may also be used in write operations and can accept external data while other RAM arrays are supplying data to the NVM for programming. For example, while the NVM layers are writing data to storage, the RAM layer may continue to receive additional data as input without waiting for the NVM write to complete. Because programming operations are typically longer than page load operations (eg. ˜300 us/page programming vs. 25 us/page for reads), data can be fully loaded into the RAM arrays before programming operations are complete.
The combined NVM and RAM stack described here may be useful for a variety of different applications, including, for example, machine learning applications. For example, machine learning applications may require weight memory, such as information that has been learned that is needed to perform computation. The weight memory can be a large amount of memory, wherein moving that weight memory burns a large amount of power. However, moving the data over the highly parallel path between the NVM in the stack and the RAM may be done over a short electrical distance, and therefore not burn as much power. Because in a machine learning application it is known ahead of time when the weight memory will be used, the weight memory can be queued and loaded into RAM for faster access. For example, if it is known that the weight memory is used sequentially to multiply accumulate (MAC) operations with activations, access to the weight memory can be queued for faster operation. Moreover, the NVM can automatically write as the RAM is exchanging information with a controller, such as an application specific integrated circuit (ASIC) or system on chip (SoC). At the same time, the NVM can also start reading the next operation.
A process for making the combined NVM and RAM stack may start with a larger layer of NVM. Multiple additional layers of NVM may be bonded thereto, for example, using DBI®. The RAM layer may then be bonded to a bottom-most layer of the NVM. The result resembles a single die that can be stacked on another die, mounted on an interposer, or packaged independently.
The NVM layers 130 may include any of a variety of types of nonvolatile memory, such as NAND flash memory, NOR flash memory, EPROM, EEPROM, magnetoresitive RAM (MRAM), phase change RAM, etc. Each layer of NVM may be bonded together using, for example, low temperature bonding techniques such as DBI®. Each layer of NVM may be, for example, approximately 50 μm thick or less. While several layers of NVM 130 are shown, it should be understood that any number of NVM layers 130 may be included in the stack. A total thickness of the NVM layers 130 may be, for example, 450 μm or less.
The RAM layer 120 may be dynamic RAM (DRAM), static RAM (SRAM), Synchronous Dynamic RAM (SDRAM), Single Data Rate Synchronous Dynamic RAM (SDR SDRAM), Double Data Rate Synchronous Dynamic RAM (DDR SDRAM, DDR2, DDR3, DDR4), Graphics Double Data Rate Synchronous Dynamic RAM (GDDR SDRAM, GDDR2, GDDR3, GDDR4, GDDR5), NRAM, RRAM, or any of a variety of other types of memory. The RAM layer 120 includes an external interface for communication with the controller 160, the external interface further providing for communication between the NVM layers 130 and the controller 160.
The RAM layer 120 is also interconnected to a bottom layer of NVM in the stack 100. For example, the RAM layer 120 may be bonded using various bonding techniques, including using direct dielectric bonding, non-adhesive techniques, such as a ZiBond® direct bonding technique, or a DBI® hybrid bonding technique, both available from Invensas Bonding Technologies, Inc. (formerly Ziptronix, Inc.), a subsidiary of Xperi Corp. (see for example, U.S. Pat. Nos. 6,864,585 and 7,485,968, which are incorporated herein in their entirety).
According to the example of
In another embodiment, shown in
In either embodiment of
According to one example, if a time to complete a read load, such as moving data from flash cells to a register, is 25 us, and a clock speed is 24 ns, moving 2112 bytes, 2 bytes at a time, will require 1056 clock cycles. 1056 cycles*24 ns/cycle=25.344 us. With this example scenario, if a first register has been filled with data, and has been sending the data out through the I/O's 2 bytes at a time, it will have completed that operation in 25.344 us. While this operation has been in progress, a 2nd register could be loaded from a new NAND flash page and could be ready to start providing its data to other devices outside the stack. The process may continue with the first register getting filled by the flash array while the 2nd register is providing data to other devices outside the stack. This allows a continuous read of data from flash memory. If the array is slower and needs more time to load a page from the flash array to its register, then a slower clock speed would be necessary to avoid dead cycles if ping ponging between two registers.
According to some examples, a capacitive layer may also be included in the combined memory stack. For example, a wafer layer may be configured to hold a charge. Just as one example, the wafer layer may include a layer of silicon with collimated pores that is covered with dielectric. The capacitive layer may be at a top of the NVM stack, furthest from system-level connections, stacked between the RAM layer and the NVM layers, or in any other layer. In the event of a power outage, the capacitive layer may provide enough power to finish writing anything remaining in the RAM layer to the NVM layers.
In some examples, the logic for determining where the data should be stored may include a machine learning algorithm. For example, data usage may be evaluated. What is learned from the evaluations may be used to better classify data that is received, wherein the classifications determine where the data is stored.
Once it is determined whether the data should be stored in long term or short term storage, the data is moved to NVM 330 or to RAM cells 322, respectively. According to some aspects, the data stored in NVM 330 and/or Ram cells 322 may be reevaluated from time to time, and moved based on the reevaluation. For example, if a first set of data stored in NVM 330 is accessed more frequently than a second set of data stored in RAM 322, the first set of data may be moved to RAM 322 and/or the second set of data may be moved to NVM 330. As another example, if the RAM 322 is becoming full, some of the data stored therein may be moved to NVM 330.
Similar to the way in which the data is moved up from the external device through the layers of the memory stack to store the data, data may be accessed by communications through the DRAM-like interface 310. Further, data may be moved down the stack and transmitted out to external devices through the DRAM-like interface 310.
An I/O path between the RAM layer and the NVM layers may be relatively wide. For example, as described in further detail below in connection with
The VM portion 420 may be a layer of RAM, such as DRAM or other type of RAM. The VM portion 420 may include a memory array 422 including a plurality of memory banks. According to some examples, the memory array 422 may include multiple groups of memory, with each group having multiple memory banks. Each memory bank may include a plurality of sense amplifiers for use when reading data from the memory. The sense amplifiers may be coupled to a global I/O gate 425, such as through I/O gating mask logic 423. The global I/O gate may further be coupled to a data interface 427 for sending or receiving communications between the RAM portion 420 and other devices outside of the stack. For example, a bus 428 between the data interface 427 and off-chip devices can be any bus width, such as 4, 8, 16, 32, 64, 128 bits wide, etc. If an internal bus width is increased, the bus 428 width may also be increased.
The NVM portion 430 includes multiple NVM planes 432 or arrays, each NVM plane 432 being coupled to NVM sense amplifiers and drivers 434 through NVM array bus 433. The NVM array bus 433 may be, for example, 16,384 bits wide with optional error correction bits. The sense amplifiers and drivers 434 are further coupled to the global I/O gating 425 through bus 435. For example, the bus 435 may be any width, such as 128 bits or wider. In some examples, the bus 435 may be 1 k bit wide, 16 k bit wide, or greater.
According to some examples, rather than sending data in page sizes, the data may be sent in error correction code (ECC) chunks. For example, ECC information in a NAND page may be matched an ECC interval in a DRAM page.
Each layer of NAND in the NAND flash array plane may include extra bits. In this regard, the DRAM may overprovision data to be stored in the NAND, such that more bits are available than those advertised. In this regard, if a NAND location is starting to fail because it is being written to too often, the data can be redirected to a different set of cells in NAND.
The RAM 520 is capable of outputting or inputting data fast enough to give a continuous pipeline of data. For example, data stored in the RAM may be retrieved in 25 μs or less. As another example, the NVM is capable of providing in parallel a page of M bits of data with a cycle time T1. The RAM memory locations can capture and store a page content within a time T2 that is less than or equal to the read cycle time T1 of the corresponding NVM flash page. According to some examples, the RAM 520 memory chip may be connected in total to N output nodes and each output node can provide a new bit of data in a time T3 that is equal to or less than T1*N/(M*P), wherein P is a number of the NVM array planes 540. The stacked memory may be capable of providing a continuous stream of output data at a data rate=1/T3. In other examples, the data rate may be greater than 1/T3/1.25. By way of example only, if a page is 16,896 bits, and there are 2-8 NVM array planes 540, the total number of output nodes N to which the RAM chip may be connected=8-16.
Each plane 606 may operate independently. Accordingly, while a first plane of the die 608 is writing data, a second plane of the same die 608 may be reading a next operation from RAM.
According to some examples, in addition to being coupled to a first type of NVM, the RAM may also be coupled to other types of NVM, such as embedded NVM (ENVM). For example, the NVM and ENVM may share a same layer of the stack, such as if the NVM and ENVM are positioned alongside one another. As another example, the ENVM may be a separate layer of the stack from the NVM.
While some examples of integrating ENVM have been described above, it should be understood that additional examples are possible. For example, ENVM may be stack between layers of NVM, logic for the ENVM may reside on the RAM, etc.
The combined RAM and NVM memory stack as described in the examples above may be used for storing data for any of a variety of applications. Just as one example, the stack may be used in machine learning applications, where weight memory is stored in the NVM layers and activations are stored in RAM. As weights and activations are input to and stored in the stack, multiply accumulate (MAC) operations may be output. The weight memory may be moved to RAM over the highly parallel path between the NVM in the stack and the RAM. Because this path is a short electrical distance, power consumption is relatively low. Moreover, such movement can be scheduled. For example, it may be determined that a first operation is always or often followed by a second operation that requires accessing the weight memory. In this regard, the weight memory may be queued into RAM each time the first operation is performed, without waiting for the second operation. Additionally, the NVM can concurrently write as the RAM is exchanging information with a controller, such as an application specific integrated circuit (ASIC) or system on chip (SoC). At the same time, the NVM can also start reading the next operation. Because different pins may be used for reading and writing at a given time, the RAM may continuously be supplied with data to store in NVM while also sending data off-chip. Each of the different pins may be bidirectional, and many different buses may be available. While the RAM is communicating with the controller, it is reading data or sending data. At the same time, internally, it could be moving data to the NVM.
Other example applications may include switching. For example, a layer of NVM may be reserved for a lookup table. Further example applications include high-performance compute applications, mobile devices, etc.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims.
The present application claims the benefit of the filing date of U.S. Provisional Patent Application No. 62/923,839 filed Oct. 21, 2019, the disclosure of which is hereby incorporated herein by reference.
Number | Name | Date | Kind |
---|---|---|---|
5753536 | Sugiyama et al. | May 1998 | A |
5771555 | Eda et al. | Jun 1998 | A |
6080640 | Gardner et al. | Jun 2000 | A |
6423640 | Lee et al. | Jul 2002 | B1 |
6465892 | Suga | Oct 2002 | B1 |
6887769 | Kellar et al. | May 2005 | B2 |
6908027 | Tolchinsky et al. | Jun 2005 | B2 |
6952368 | Miura et al. | Oct 2005 | B2 |
7045453 | Canaperi et al. | May 2006 | B2 |
7105980 | Abbott et al. | Sep 2006 | B2 |
7193423 | Dalton et al. | Mar 2007 | B1 |
7554830 | Miura et al. | Jun 2009 | B2 |
7750488 | Patti et al. | Jul 2010 | B2 |
7803693 | Trezza | Sep 2010 | B2 |
7872895 | Miura et al. | Jan 2011 | B2 |
8183127 | Patti et al. | May 2012 | B2 |
8349635 | Gan et al. | Jan 2013 | B1 |
8377798 | Peng et al. | Feb 2013 | B2 |
8397013 | Rosenband et al. | Mar 2013 | B1 |
8432716 | Miura et al. | Apr 2013 | B2 |
8441131 | Ryan | May 2013 | B2 |
8476165 | Trickett et al. | Jul 2013 | B2 |
8482132 | Yang et al. | Jul 2013 | B2 |
8501537 | Sadaka et al. | Aug 2013 | B2 |
8524533 | Tong et al. | Sep 2013 | B2 |
8611123 | Koh | Dec 2013 | B2 |
8620164 | Heck et al. | Dec 2013 | B2 |
8647987 | Yang et al. | Feb 2014 | B2 |
8697493 | Sadaka | Apr 2014 | B2 |
8716105 | Sadaka et al. | May 2014 | B2 |
8802538 | Liu | Aug 2014 | B1 |
8809123 | Liu et al. | Aug 2014 | B2 |
8841002 | Tong | Sep 2014 | B2 |
9093350 | Endo et al. | Jul 2015 | B2 |
9142517 | Liu et al. | Sep 2015 | B2 |
9171756 | Enquist et al. | Oct 2015 | B2 |
9184125 | Enquist et al. | Nov 2015 | B2 |
9224704 | Landru | Dec 2015 | B2 |
9230941 | Chen et al. | Jan 2016 | B2 |
9257399 | Kuang et al. | Feb 2016 | B2 |
9299736 | Chen et al. | Mar 2016 | B2 |
9312229 | Chen et al. | Apr 2016 | B2 |
9331149 | Tong et al. | May 2016 | B2 |
9337235 | Chen et al. | May 2016 | B2 |
9385024 | Tong et al. | Jul 2016 | B2 |
9394161 | Cheng et al. | Jul 2016 | B2 |
9431368 | Enquist et al. | Aug 2016 | B2 |
9437572 | Chen et al. | Sep 2016 | B2 |
9443796 | Chou et al. | Sep 2016 | B2 |
9461007 | Chun et al. | Oct 2016 | B2 |
9496239 | Edelstein et al. | Nov 2016 | B1 |
9536848 | England et al. | Jan 2017 | B2 |
9559081 | Lai et al. | Jan 2017 | B1 |
9620481 | Edelstein et al. | Apr 2017 | B2 |
9656852 | Cheng et al. | May 2017 | B2 |
9723716 | Meinhold | Aug 2017 | B2 |
9728521 | Tsai et al. | Aug 2017 | B2 |
9741620 | Uzoh et al. | Aug 2017 | B2 |
9754667 | Alsmeier | Sep 2017 | B2 |
9799587 | Fujii et al. | Oct 2017 | B2 |
9852988 | Enquist et al. | Dec 2017 | B2 |
9859004 | Alsmeier | Jan 2018 | B1 |
9893004 | Yazdani | Feb 2018 | B2 |
9899442 | Katkar | Feb 2018 | B2 |
9929050 | Lin | Mar 2018 | B2 |
9941241 | Edelstein et al. | Apr 2018 | B2 |
9941243 | Kim et al. | Apr 2018 | B2 |
9953941 | Enquist | Apr 2018 | B2 |
9960142 | Chen et al. | May 2018 | B2 |
10002844 | Wang et al. | Jun 2018 | B1 |
10026605 | Doub et al. | Jul 2018 | B2 |
10075657 | Fahim et al. | Sep 2018 | B2 |
10204893 | Uzoh et al. | Feb 2019 | B2 |
10269756 | Uzoh | Apr 2019 | B2 |
10276619 | Kao et al. | Apr 2019 | B2 |
10276909 | Huang et al. | Apr 2019 | B2 |
10418277 | Cheng et al. | Sep 2019 | B2 |
10446456 | Shen et al. | Oct 2019 | B2 |
10446487 | Huang et al. | Oct 2019 | B2 |
10446532 | Uzoh et al. | Oct 2019 | B2 |
10508030 | Katkar et al. | Dec 2019 | B2 |
10522499 | Enquist et al. | Dec 2019 | B2 |
10707087 | Uzoh et al. | Jul 2020 | B2 |
10784191 | Huang et al. | Sep 2020 | B2 |
10790262 | Uzoh et al. | Sep 2020 | B2 |
10840135 | Uzoh | Nov 2020 | B2 |
10840205 | Fountain, Jr. et al. | Nov 2020 | B2 |
10854578 | Morein | Dec 2020 | B2 |
10879212 | Uzoh et al. | Dec 2020 | B2 |
10886177 | DeLaCruz et al. | Jan 2021 | B2 |
10892246 | Uzoh | Jan 2021 | B2 |
10923408 | Huang et al. | Feb 2021 | B2 |
10923413 | DeLaCruz | Feb 2021 | B2 |
10950547 | Mohammed et al. | Mar 2021 | B2 |
10964664 | Mandalapu et al. | Mar 2021 | B2 |
10985133 | Uzoh | Apr 2021 | B2 |
10991804 | DeLaCruz et al. | Apr 2021 | B2 |
10998292 | Lee et al. | May 2021 | B2 |
11004757 | Katkar et al. | May 2021 | B2 |
11011494 | Gao et al. | May 2021 | B2 |
11011503 | Wang et al. | May 2021 | B2 |
11031285 | Katkar et al. | Jun 2021 | B2 |
11037919 | Uzoh et al. | Jun 2021 | B2 |
11056348 | Theil | Jul 2021 | B2 |
11069734 | Katkar | Jul 2021 | B2 |
11088099 | Katkar et al. | Aug 2021 | B2 |
11127738 | DeLaCruz et al. | Sep 2021 | B2 |
11158573 | Uzoh et al. | Oct 2021 | B2 |
11158606 | Gao et al. | Oct 2021 | B2 |
11169326 | Huang et al. | Nov 2021 | B2 |
11171117 | Gao et al. | Nov 2021 | B2 |
11176450 | Teig et al. | Nov 2021 | B2 |
11195748 | Uzoh et al. | Dec 2021 | B2 |
11205625 | DeLaCruz et al. | Dec 2021 | B2 |
11244920 | Uzoh | Feb 2022 | B2 |
11256004 | Haba et al. | Feb 2022 | B2 |
11264357 | DeLaCruz et al. | Mar 2022 | B1 |
11276676 | Enquist et al. | Mar 2022 | B2 |
11296044 | Gao et al. | Apr 2022 | B2 |
11296053 | Uzoh et al. | Apr 2022 | B2 |
11329034 | Tao et al. | May 2022 | B2 |
11348898 | DeLaCruz et al. | May 2022 | B2 |
11355404 | Gao et al. | Jun 2022 | B2 |
11355443 | Huang et al. | Jun 2022 | B2 |
11367652 | Uzoh et al. | Jun 2022 | B2 |
11373963 | DeLaCruz et al. | Jun 2022 | B2 |
11380597 | Katkar et al. | Jul 2022 | B2 |
11385278 | DeLaCruz et al. | Jul 2022 | B2 |
11387202 | Haba et al. | Jul 2022 | B2 |
11387214 | Wang et al. | Jul 2022 | B2 |
11393779 | Gao et al. | Jul 2022 | B2 |
11462419 | Haba | Oct 2022 | B2 |
11476213 | Haba et al. | Oct 2022 | B2 |
11515291 | DeLaCruz et al. | Nov 2022 | B2 |
20040084414 | Sakai et al. | May 2004 | A1 |
20060057945 | Hsu et al. | Mar 2006 | A1 |
20070111386 | Kim et al. | May 2007 | A1 |
20140175655 | Chen et al. | Jun 2014 | A1 |
20150064498 | Tong | Mar 2015 | A1 |
20160343682 | Kawasaki | Nov 2016 | A1 |
20170338214 | Uzoh | Nov 2017 | A1 |
20180175012 | Wu et al. | Jun 2018 | A1 |
20180182639 | Uzoh et al. | Jun 2018 | A1 |
20180182666 | Uzoh et al. | Jun 2018 | A1 |
20180190580 | Haba et al. | Jul 2018 | A1 |
20180190583 | DeLaCruz et al. | Jul 2018 | A1 |
20180219038 | Gambino et al. | Aug 2018 | A1 |
20180323177 | Yu et al. | Nov 2018 | A1 |
20180323227 | Zhang et al. | Nov 2018 | A1 |
20180331066 | Uzoh et al. | Nov 2018 | A1 |
20190115277 | Yu et al. | Apr 2019 | A1 |
20190131277 | Yang et al. | May 2019 | A1 |
20190245543 | Lee | Aug 2019 | A1 |
20190333550 | Fisch | Oct 2019 | A1 |
20190385935 | Gao et al. | Dec 2019 | A1 |
20200013765 | Fountain, Jr. et al. | Jan 2020 | A1 |
20200035641 | Fountain, Jr. et al. | Jan 2020 | A1 |
20200294908 | Haba et al. | Sep 2020 | A1 |
20200328162 | Haba et al. | Oct 2020 | A1 |
20200395321 | Katkar et al. | Dec 2020 | A1 |
20210098412 | Haba et al. | Apr 2021 | A1 |
20210143125 | DeLaCruz et al. | May 2021 | A1 |
20210181510 | Katkar et al. | Jun 2021 | A1 |
20210193603 | DeLaCruz et al. | Jun 2021 | A1 |
20210193624 | DeLaCruz et al. | Jun 2021 | A1 |
20210193625 | Katkar et al. | Jun 2021 | A1 |
20210242152 | Fountain, Jr. et al. | Aug 2021 | A1 |
20210296282 | Gao et al. | Sep 2021 | A1 |
20210305202 | Uzoh et al. | Sep 2021 | A1 |
20210366820 | Uzoh | Nov 2021 | A1 |
20210407941 | Haba | Dec 2021 | A1 |
20220077063 | Haba | Mar 2022 | A1 |
20220077087 | Haba | Mar 2022 | A1 |
20220139867 | Uzoh | May 2022 | A1 |
20220139869 | Gao et al. | May 2022 | A1 |
20220208650 | Gao et al. | Jun 2022 | A1 |
20220208702 | Uzoh | Jun 2022 | A1 |
20220208723 | Katkar et al. | Jun 2022 | A1 |
20220246497 | Fountain, Jr. et al. | Aug 2022 | A1 |
20220285303 | Mirkarimi et al. | Sep 2022 | A1 |
20220319901 | Suwito et al. | Oct 2022 | A1 |
20220320035 | Uzoh et al. | Oct 2022 | A1 |
20220320036 | Gao et al. | Oct 2022 | A1 |
20230005850 | Fountain, Jr. | Jan 2023 | A1 |
20230019869 | Mirkarimi et al. | Jan 2023 | A1 |
20230036441 | Haba et al. | Feb 2023 | A1 |
20230067677 | Lee et al. | Mar 2023 | A1 |
20230069183 | Haba | Mar 2023 | A1 |
20230100032 | Haba et al. | Mar 2023 | A1 |
20230115122 | Uzoh et al. | Apr 2023 | A1 |
20230122531 | Uzoh | Apr 2023 | A1 |
20230123423 | Gao et al. | Apr 2023 | A1 |
20230125395 | Gao et al. | Apr 2023 | A1 |
20230130259 | Haba et al. | Apr 2023 | A1 |
20230132632 | Katkar et al. | May 2023 | A1 |
20230140107 | Uzoh et al. | May 2023 | A1 |
20230142680 | Guevara et al. | May 2023 | A1 |
20230154816 | Haba et al. | May 2023 | A1 |
20230154828 | Haba et al. | May 2023 | A1 |
20230187264 | Uzoh et al. | Jun 2023 | A1 |
20230187317 | Uzoh | Jun 2023 | A1 |
20230187412 | Gao et al. | Jun 2023 | A1 |
20230197453 | Fountain, Jr. et al. | Jun 2023 | A1 |
20230197496 | Theil | Jun 2023 | A1 |
20230197559 | Haba et al. | Jun 2023 | A1 |
20230197560 | Katkar et al. | Jun 2023 | A1 |
20230197655 | Theil et al. | Jun 2023 | A1 |
20230207402 | Fountain, Jr. et al. | Jun 2023 | A1 |
20230207437 | Haba | Jun 2023 | A1 |
20230207474 | Uzoh et al. | Jun 2023 | A1 |
20230207514 | Gao et al. | Jun 2023 | A1 |
20230215836 | Haba et al. | Jul 2023 | A1 |
20230245950 | Haba et al. | Aug 2023 | A1 |
20230268300 | Uzoh et al. | Aug 2023 | A1 |
Number | Date | Country |
---|---|---|
2013-033786 | Feb 2013 | JP |
2018-160519 | Oct 2018 | JP |
WO 2005043584 | May 2005 | WO |
Entry |
---|
Ker, Ming-Dou et al., “Fully process-compatible layout design on bond pad to improve wire bond reliability in CMOS Ics,” IEEE Transactions on Components and Packaging Technologies, Jun. 2002, vol. 25, No. 2, pp. 309-316. |
Moriceau, H. et al., “Overview of recent direct wafer bonding advances and applications,” Advances in Natural Sciences-Nanoscience and Nanotechnology, 2010, 11 pages. |
Nakanishi, H. et al., “Studies on SiO2-SiO2 bonding with hydrofluoric acid. Room temperature and low stress bonding technique for MEMS,” Sensors and Actuators, 2000, vol. 79, pp. 237-244. |
Oberhammer, J. et al., “Sealing of adhesive bonded devices on wafer level,” Sensors and Actuators A, 2004, vol. 110, No. 1-3, pp. 407-412, see pp. 407-412, and Figures 1 (a)-1 (I), 6 pages. |
Plobi, A. et al., “Wafer direct bonding: tailoring adhesion between brittle materials,” Materials Science and Engineering Review Journal, 1999, R25, 88 pages. |
Bush, Steve, “Electronica: Automotive power modules from on Semi,” ElectronicsWeekly.com, indicating an ONSEMI AR0820 product was to be demonstrated at a Nov. 2018 trade show, https://www.electronicsweekly.com/news/products/power-supplies/electronica-automotive-power-modules-semi-2018-11/ (published Nov. 8, 2018; downloaded Jul. 26, 2023). 1 page. |
Morrison, Jim et al., “Samsung Galaxy S7 Edge Teardown,” Tech Insights (posted Apr. 24, 2016), includes description of hybrid bonded Sony IMX260 dual-pixel sensor, https://www.techinsights.com/blog/samsung-galaxy-s7-edge-teardown, downloaded Jul. 11, 2023, 9 pages. |
ONSEMI AR0820 image, cross section of a CMOS image sensor product. The part in the image was shipped on Sep. 16, 2021. Applicant makes no representation that the part in the image is identical to the part identified in the separately submitted reference BUSH, Nov. 8, 2018, ElectronicsWeekly.com (“BUSH article”); however, the imaged part and the part shown in the BUSH article share the part number “ONSEMI AR0820.”, 3 pages. |
Sony IMX260 image, cross section of Sony dual-pixel sensor product labeled IMX260, showing peripheral probe and wire bond pads in a bonded structure. The part in the image was shipped in Apr. 2016. Applicant makes no representation that the part in the image is identical to the part identified in the separately submitted reference Morrison et al. (Tech Insights article dated Apr. 24, 2016), describing and showing a similar sensor product within the Samsung Galaxy S7; however the imaged part and the part shown in the Morrison et al. article share the part name “Sony IMX260.”, 1 page. |
Number | Date | Country | |
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20210118864 A1 | Apr 2021 | US |
Number | Date | Country | |
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62923839 | Oct 2019 | US |