The following prior applications are herein incorporated by reference in their entirety for all purposes:
U.S. Pat. No. 9,100,232, filed Feb. 2, 2105 as application Ser. No. 14/612,241 and issued Aug. 4, 2015, naming Amin Shokrollahi, Ali Hormati, and Roger Ulrich, entitled “Method and Apparatus for Low Power Chip-to-Chip Communications with Constrained ISI Ratio”, hereinafter identified as [Shokrollahi].
U.S. patent application Ser. No. 15/582,545, filed Apr. 28, 2017, naming Ali Hormati and Richard Simpson, entitled “Clock Data Recovery Utilizing Decision Feedback Equalization”, hereinafter identified as [Hormati I].
U.S. patent application Ser. No. 16/800,892, filed Feb. 25, 2020, naming Ali Hormati, entitled “Sampler Offset Calibration during Operation”, hereinafter identified as [Hormati II].
U.S. patent application Ser. No. 16/833,362, filed Mar. 27, 2020, naming Ali Hormati, entitled “Variable Gain Amplifier and Sampler Offset Calibration without Clock Recovery”, hereinafter identified as [Hormati III].
In modern digital systems, digital information has to be processed in a reliable and efficient way. In this context, digital information is to be understood as information available in discrete, i.e., discontinuous values. Bits, collection of bits, but also numbers from a finite set can be used to represent digital information.
In most chip-to-chip, or device-to-device communication systems, communication takes place over a plurality of wires to increase the aggregate bandwidth. A single or pair of these wires may be referred to as a channel or link and multiple channels create a communication bus between the electronic components. At the physical circuitry level, in chip-to-chip communication systems, buses are typically made of electrical conductors in the package between chips and motherboards, on printed circuit boards (“PCBs”) boards or in cables and connectors between PCBs. In high frequency applications, microstrip or stripline PCB traces may be used.
Common methods for transmitting signals over bus wires include single-ended and differential signaling methods. In applications requiring high speed communications, those methods can be further optimized in terms of power consumption and pin-efficiency, especially in high-speed communications. More recently, vector signaling methods such as described in [Shokrollahi] have been proposed to further optimize the trade-offs between power consumption, pin efficiency and noise robustness of chip-to-chip communication systems. In those vector signaling systems, digital information at the transmitter is transformed into a different representation space in the form of a vector codeword that is chosen in order to optimize the power consumption, pin-efficiency and speed trade-offs based on the transmission channel properties and communication system design constraints. Herein, this process is referred to as “encoding”. The encoded codeword is communicated as a group of signals from the transmitter to one or more receivers. At a receiver, the received signals corresponding to the codeword are transformed back into the original digital information representation space. Herein, this process is referred to as “decoding”.
Regardless of the encoding method used, the received signals presented to the receiving device are sampled (or their signal value otherwise recorded) at intervals best representing the original transmitted values, regardless of transmission channel delays, interference, and noise. This Clock and Data Recovery (CDR) not only must determine the appropriate sample timing, but must continue to do so continuously, providing dynamic compensation for varying signal propagation conditions. It is common for communications receivers to extract a receive clock signal from the received data stream. Some communications protocols facilitate such Clock Data Recovery or CDR operation by constraining the communications signaling so as to distinguish between clock-related and data-related signal components. Similarly, some communications receivers process the received signals beyond the minimum necessary to detect data, so as to provide the additional information to facilitate clock recovery. As one example, a so-called double-baud-rate receive sampler may measure received signal levels at twice the expected data reception rate, to allow independent detection of the received signal level corresponding to the data component, and the chronologically offset received signal transition related to the signal clock component.
However, the introduction of extraneous communications protocol transitions is known to limit achievable data communication rate. Similarly, receive sampling at higher than transmitted data rate is known to substantially increase receiver power utilization.
Real-world communications channels are imperfect, degrading transmitted signals in both amplitude (e.g. attenuation) and timing (e.g. delay and pulse smearing) which may be addressed via transmitter pre-compensation and/or receive equalization. Continuous time linear equalization (CTLE) is one known approach to frequency domain equalization, in one example providing compensation for increased channel attenuation at high frequencies. Time-domain-oriented equalization methods are also used to compensate for the effects of inter-symbol-interference or ISI on the received signal. Such ISI is caused by the residual electrical effects of a previously transmitted signal persisting in the communications transmission medium, so as to affect the amplitude or timing of the current symbol interval. As one example, a transmission line medium having one or more impedance anomalies may introduce signal reflections. Thus, a transmitted signal will propagate over the medium and be partially reflected by one or more such anomalies, with such reflections appearing at the receiver at a later time in superposition with signals propagating directly.
One method of data-dependent receive equalization is Decision Feedback Equalization or DFE. Here, the time-domain oriented equalization is performed by maintaining a history of previously-received data values at the receiver, which are processed by a transmission line model to predict the expected influence that each of the historical data values would have on the present receive signal. Such a transmission line model may be pre-calculated, derived by measurement, or generated heuristically, and may encompass the effects of one or more than one previous data interval. The predicted influence of these one or more previous data intervals is collectively called the DFE compensation. At low to moderate data rates, the DFE compensation may be calculated in time to be applied before the next data sample is detected, as example by being explicitly subtracted from the received data signal prior to receive sampling, or implicitly subtracted by modifying the reference level to which the received data signal is compared in the receive data sampler or comparator. However, at higher data rates the detection of previous data bits and computation of the DFE compensation may not be complete in time for the next data sample, requiring use of so-called “unrolled” DFE computations performed on speculative or potential data values rather than known previous data values. As one example, an unrolled DFE stage may predict two different compensation values depending on whether the determining data bit will resolve to a one or a zero, with the receive detector performing sampling or slicing operations based on each of those predictions, the multiple results being maintained until the DFE decision is resolved.
A digital receiver system samples received signals in both amplitude and time, obtaining sufficient information to permit accurate detection and decoding of the transmitted data regardless of signal degradations induced by the communications medium. Addressing the particular characteristics of the communications medium may require signal amplification, frequency- and time-domain filtering, as well as accurate adjustment of both the time and amplitude at which sampling occurs.
Accurate setting of data sampler timing to maximize receive data integrity requires accurate measurement of the overall receive eye opening. However, in some system embodiments a determination of the overall extent of the eye opening may not be made directly, due to limitations in the adjustment range of sampler timing, threshold, or both.
A method is described for indirect measurement of eye extent, including the steps of sampling a data stream at a locked sampling point using data samplers having vertical decision thresholds associated with speculative decision feedback equalization (DFE) terms to detect a predetermined transitional data pattern, varying a sampling offset and a vertical threshold of a spare sampler to measure a signal amplitude trajectory of a pattern-verified signal according to detection of the predetermined transitional data pattern, and adjusting the locked sampling point based on the measured signal amplitude trajectory by adjusting the speculative DFE terms.
In recent years, the signaling rate of high-speed communications systems have reached speeds of tens of gigabits per second, with individual data unit intervals measured in picoseconds. One example of such a system is given by [Shokrollahi], which describes use of vector signaling codes over extremely high bandwidth multiwire data communications links, such as between two integrated circuit devices in a system. Depending on the particular coding scheme used, the number of channels comprising such a communications link may range from two to eight or more, and may also communicate one or more clock signals, either within data channels or on separate communications channels.
In one embodiment utilizing a vector signaling code, multiple bits of data are encoded at the transmitter into a vector signaling “codeword”, i.e. a set of symbols to be transmitted essentially simultaneously over the multiple wires or channels of the communication medium. As each such wire or channel may take on more than two possible values, each symbol of the codeword is drawn from an alphabet of allowable signal values; in examples of [Shokrollahi], alphabets of four and ten values are used in encodings of five data bits into six symbol codewords. In the receiver, the multilevel wire signals are detected to determine the received codeword, which is then decoded (e.g. by a mapping table lookup) into received data.
In an alternative embodiment, it is noted that each vector signaling codeword is a superposition of “subchannel” components, each such subchannel being an orthogonal mode or pattern of modulation of the wires or channels. Thus, in the example of [Shokrollahi], five subchannels may be summed to produce the transmitted signals, each subchannel modulated by one of the five transmitted data bits. Similarly, a vector signaling code receiver may directly detect the combination of received wire signals corresponding to a particular subchannel, as one example by using a multi-input comparator (MIC) performing a weighted summation of two or more wire signals correlated with the orthogonal mode of that subchannel, and directly producing one bit of received data. In the example of [Shokrollahi], full decoding of five data bits is shown using a set of MICs combining from two to six wire signals. As codeword and subchannel processing models are fully equivalent, interoperation is assured regardless of the particular encoding and decoding model used, e.g. allowing combination of a codeword transmitter with a MIC-based subchannel receiver based on the same orthogonal vector signaling code.
As previously mentioned, wire signals in an orthogonal vector signaling code system may take on multiple distinct values, while detected subchannel results (as one example, the results of weighted summation as at the output of a MIC) are typically binary, thus receive processing functions such as ISI reduction and skew correction may be performed more efficiently on the simpler subchannel signals rather than on the more complex wire signals.
Conventional practice for a high-speed integrated circuit receiver terminates each received signal channel, subchannel, or wire signal in a signal detector. This signal detector performs a measurement constrained in both time and amplitude dimensions; in one example embodiment, it may be composed of a sample-and-hold circuit that constrains the time interval being measured, followed by a threshold detector or digital comparator that determines whether the signal within that interval falls above or below (or in some embodiments, within bounds set by) a reference value. Alternatively, a digital comparator may determine the signal amplitude followed by a clocked digital flip-flop capturing the result at a selected time. In other embodiments, a combined time- and amplitude-detection circuit is used, measuring the amplitude state of its input in response to the specified timing of a clock transition.
For descriptive convenience, this document will use the term sampling device, or more simply “sampler” to describe the receiver component that obtains an input measurement, as it implies both the time and amplitude measurement constraints, rather than the equivalent but less descriptive term “slicer” also used in the art. Similarly, the sampler input will simply be described as the “received signal”, whether it is derived from a wire signal, a MIC subchannel output, or other received information value. No limitation is implied by these descriptive conveniences, with all embodiments described herein being applicable to all signal sources and encodings.
In some embodiments, the time at which a sample is captured may be adjusted in some or all of the receiver samplers; in some embodiments, the threshold level to which a sample is compared may be adjusted in some or all of the receiver samplers. As one example, the well-known receiver “eye plot” diagram is typically obtained by iterative adjustment of these parameters, with the results plotted graphically as signal amplitudes over time.
Clock Data Recovery
Clock Data Recovery or Clock Data Alignment (CDR or CDA) circuits as in [Hormati I] extract timing information, either from the received signal(s) themselves or from dedicated clock signal inputs, and utilizing that extracted information to generate clock signals to control the time interval used by received signal sampling device. The actual clock extraction may be performed using well known circuits such as a Phase Locked Loop (PLL) or Delay Locked Loop (DLL), which in their operation may also generate higher frequency internal clocks, multiple clock phases, etc. in support of receiver operation. Implementation distinctions between CDR and CDA embodiments as described in the art are irrelevant to the present descriptions, thus the term CDA will subsequently be used herein as a generic identifier, without implying limitation.
In one common CDA embodiment, a first sample time is configured to optimally obtain the data sample, and a second sample time is configured to optimally determine whether the phase of the internal clock remains aligned with incoming signal transitions, which may be as much as ½ of a received signal unit interval (UI) offset in time from the optimum data sampling time. As sampling in such embodiments occurs twice per received unit interval, such systems are described as utilizing a double baud rate CDA. Such systems are very common in low speed communications system or where the received signal exhibits very sharp transitions, i.e. where there is significant displacement in time between observation of a signal transition and optimum sampling of data.
So-called single baud rate CDA embodiments are also known, in which the same sample time is used to obtain the data sample, and to determine whether the phase of the internal clock remains aligned with incoming signal transitions. In one such embodiment, inter-symbol interference (ISI) within the transmission medium, combined with group delay within the receive input processing, can result in detectable signal transitions which can inform CDA alignment at one sampling amplitude, while simultaneously detecting a stable data value at a second sampling amplitude.
CDA embodiments produce a single or primary sampling clock that provides a phase-locked sampling point for one or more data samplers. In some embodiments, the CDA generates early and late measurements and responsively adjusts the sampling instant to find a locked sampling instant representative of a desired ratio of early and late measurements. A CDA embodiment may also produce secondary clocks having predetermined phase relationships to the primary clock, as one example having ninety degree or quadrature offsets. In some embodiments, two such clocks may be input to phase interpolation (PI) circuits, allowing creation of additional phase-locked sampling points having a configurable phase relationship to the primary sampling clock and its associated data samplers.
Receive Signal Equalization
At high data rates, even relatively short and high-quality communications channels exhibit considerable frequency-dependent signal loss, thus it is common for data receivers to incorporate receive signal equalization. Continuous-time Linear Equalization (CTLE) is commonly used to provide increased high frequency gain in the receive signal path, in compensation for the increased high frequency attenuation of the channel.
It has also become common practice for data communications receivers to incorporate Decision Feedback Equalization (DFE) to compensate for signal propagation anomalies in the communications medium, including ISI. The DFE system performs non-linear time-domain equalization on the received signal by maintaining a history of previously-received data values at the receiver, and processing those historic data values with a transmission line model to predict the expected influence each of the historical data values would have on the present receive signal. Such a transmission line model may be pre-calculated, derived by measurement, or generated heuristically, and may encompass the effects of one or more than one previous data interval.
In a typical receiver design, this computed DFE compensation value will be subtracted from the current receive signal input to produce a corrected signal more accurately representing the received data value. Those familiar with the art will recognize that the DFE compensation value produced as described above cannot be calculated until the previous unit interval's data value has been detected. Thus, as data rates increase, a point will be reached at which the information to produce the DFE compensation value is not available in time to be applied to the next unit interval sampling. Indeed, at the highest data rates currently used in practice, this situation may exist for multiple previous unit intervals, as the detection time for a single data value may represent multiple unit interval durations, requiring the receiver to pipeline or parallelize the detection operation. Thus, it is common for embodiments to forgo such “closed loop” DFE methods for one or more of the most recent unit intervals, instead relying on an “open loop” or “unrolled loop” generation of one or more elements of the DFE compensation value for these most recent unit intervals.
In an effort to accelerate such DFE operation, some embodiments speculatively produce DFE compensation values corresponding to each of the possible detected data values for a given unit interval. One embodiment incorporates multiple data detection samplers, each provided with a distinct value of DFE compensation associated with the possible detected data values for one or more previous unit intervals. The result of each sampler is stored until the previous data value is known, at which time the corresponding stored result is selected for data detection.
The set of DFE compensation values speculatively created to represent the constellation of potential detected data results over the previous transmit unit interval or intervals represent a set of measurement levels spanning some portion of the receive signal amplitude range. As an example, previous transmission of consecutive “zero” signals might lead to a predicted lower threshold level for a subsequent receiver data measurement incorporating speculative DFE compensation, while previous transmission of consecutive “one” signals might lead to a predicted higher threshold level for the same data measurement. Thus, for any data measurement used to detect an actual data value, the described multiple-sampler receiver will potentially perform measurement operations using thresholds either too high or too low for the actual signal during that interval.
CDA Combined with DFE
In high speed communications systems operating over channels with significant frequency-dependent attenuation, received signals often have significantly sloped rise and fall times, even after receive signal equalization. Thus, a signal sampler timed to trigger at “center of eye” may under some circumstances still intersect with a signal transition still transitioning from one data value to the next, especially if that received signal is significantly perturbed by ISI. One such example may be seen in
In such environments, it is possible to utilize a single sample time per received unit interval to determine both data value and clock phase. These baud-rate CDA embodiments rely on the observation that certain combinations of received ISI and detection sampling threshold have sub-optimal data sampling characteristics; that is, they have a high probability of intersecting with a changing input signal having a slow rise and fall time. Thus, by controlling the receive equalization to constrain transition rates, and then restricting observation of clock timing to only those sampling thresholds and received data patterns (which correlate to particular ISI levels) that provide such intersections, a single sampling time may be utilized for both clock and data sampling.
One embodiment described in [Hormati I] takes advantage of this effect to utilize measurement operations from multiple samplers or comparators performing speculative DFE operations. In that embodiment, a stored speculative result not used for determining the received data value (that is, measured at a signal offset above or below the appropriate DFE correction for that interval, but at the same time as the valid data sample) provides information relating to clock recovery.
For purposes of description and without implying limitation, a serial data receiver as shown in
In
As would be expected, the [1, 1, 1] trajectories in
The upper DFE sampler location selected by a DFE system to detect the current data value if the previous data value was “1” is shown with the black symbol + labeled “+VH1”. It may be noted that this sampler location is well positioned in the center of the upper data eye, but also is directly over the trajectory of a [0,1,1] received signal (the current data value of which will be detected by the lower sampler location, as determined by the previous data value of “0”.) Thus, the sampler having the decision threshold set to “−VH1” (indicated by the white + symbol), effectively corresponds to an edge sample that may be utilized by the CDR system to determine whether the sampler timing is early or late relative to that signal transition. Use of sampler outputs as early-late indications causes the sampling clock to have a lock point associated with the DFE correction factors ±VH1, as the CDR will adjust the phase of the sampling clock until the early-late indications selected from the data samplers responsive to the transition data patterns are approximately a 1:1 ratio.
Dynamic Data Sampler Decision Threshold Adjustment
As previously mentioned, reliable and error-free detection of received data signals may include accurate adjustment of a data sampler threshold at a predetermined time and amplitude position within the receive signal “eye”. Drift of that predetermined sampler vs. signal relationship over time, temperature, or supply voltage can lead to an increased receive bit error rate, and ultimately detection failure.
One known solution calibrates and adjusts a spare sampler offline (i.e. in a nonintrusive manner) and then exchanges that preconfigured unit with the active data sampler, freeing it to be calibrated and adjusted. In such a system, switching circuitry must be provided to all signals entering, controlling, and output by the samplers so that they may be directed as required to either data path or calibration functions.
To avoid use of such switching circuitry, one embodiment performs measurements using a spare sampler operating outside of a data signal processing path, and then uses information obtained through such measurements to adjust operation of the data samplers operating in the data signal processing path in a nonintrusive manner, as described in [Hormati II]. As the spare sampler is not part of the active data signal processing path, threshold levels and clock timing may be adjusted without impacting received data, allowing identification of both the extremes of normal operation (i.e. the boundaries of the received “eye” opening). In some embodiments, such adjustments to the spare sampler are comparable to those used to obtain the statistical data required to plot an eye diagram, and thus that spare sampler may subsequently be referred to as an eye sampler hereinafter, without implying limitation.
Ideally, the locked sampling point for the data samplers is set to the mid-point of the maximum horizontal eye opening, which may correspond to a known signaling interval duration. (In embodiments using predictive or speculative DFE, as in these examples, the eye openings being measured are those associated with the anticipated data value detected by each sampler, e.g. the extent of a valid data “1” for the upper sampler or a valid data “0” for the lower sampler.) In one representative embodiment, this point is determined by first advancing then retarding the sample timing of a sampler (as one example, by adjusting a phase interpolator (PI) producing a secondary or phase-adjustable clock controlling the sample time of a spare sampler,) to determine the extent of the eye opening, then calculating the mid-point of those timing extremes. However, due to the unavoidable adjustment nonlinearities of an uncalibrated clock interpolator, simply averaging the two setting values is not sufficient. Thus, in the illustration of
Beyond the adjustment nonlinearity of the clock interpolator, in some embodiments sampler measurements at the left (e.g. “early”) edge of the eye may themselves be inaccurate, due to variations in sampler sensitivity due to the shorter time between the sampler being reset or initialized and when it is triggered.
In an alternative embodiment, the slope of the signal amplitude trajectory forming the left edge of the eye is measured, allowing first the effective eye edge at the desired vertical decision threshold, then the optimum sampling point of the data sampler to be determined computationally rather than by direct measurement. As shown in
For a one-stage speculative DFE as used in these examples, it may be noted that the first term of the DFE correction series H1 corresponds to approximately ½ΔV1 from the vertical center of the eye in this example. This implies that a spare pair of samplers such as 180 of
The vertical threshold voltage and the phase interpolator (PI) control values of the spare sampler determining the timing of each measurement may be configured by a dedicated measurement controller or processor, as in 190 of
Given at least two measurements ΔV1 and ΔV2 with respect to the speculative H1 DFE term (or alternatively, finite voltage measurements) at corresponding distinct sampling offsets as in
In some embodiments, the locked sampling point for the data samplers is adjusted to be approximately a half unit interval away from the left predicted intercept point at the desired vertical decision threshold. In another embodiment, a comparable measurement of two or more two-dimensional points followed by an extrapolation is used to find a rightmost predicted intercept point, and the locked sample point for the data samplers is adjusted to be half way between the leftmost and rightmost predicted intercept point at the desired vertical decision threshold. As described above, the locked sampling point may be moved horizontally throughout the signaling interval by means of adjusting the values of the speculative DFE decision thresholds provided to the data samplers. Based on the analysis of the predicted intercept point, the DFE circuit 150 may increase or decrease the magnitude of the H1 values provided to the data samplers, and the process may repeat until it is determined that the predicted intercept point is within a threshold of a known distance from the locked sampling point.
In some embodiments, measuring the signal amplitude trajectory of the pattern-verified signal comprises generating at least two signal amplitudes of the pattern-verified signal, each signal amplitude generated at respective sampling point. Two such points are shown in
The first and second intersections may be provided to measurement controller 190, which may include a processor for estimating a horizontal offset (e.g., a PI code) that would be associated with a signal amplitude on the measured signal amplitude trajectory corresponding to one of the speculative DFE terms. In the pattern verified signal [1, 0, 0] of
In some embodiments this adjustment is made by adjusting the speculative DFE term corresponding to the vertical sampling thresholds of one stage of unrolled or speculative DFE samplers. This adjustment influences the point on the signal trajectory captured for purposes of clock adjustment, moving the CDA lock phase. An alternative embodiment may adjust the sampling lock point phase directly, as with adjustment of a phase interpolator for the sampling clock.
Number | Name | Date | Kind |
---|---|---|---|
4839907 | Saneski | Jun 1989 | A |
5266907 | Dacus | Nov 1993 | A |
5302920 | Bitting | Apr 1994 | A |
5528198 | Baba et al. | Jun 1996 | A |
5565817 | Lakshmikumar | Oct 1996 | A |
5602884 | Wieczorkiewicz et al. | Feb 1997 | A |
5629651 | Mizuno | May 1997 | A |
5802356 | Gaskins et al. | Sep 1998 | A |
6002717 | Gaudet | Dec 1999 | A |
6026134 | Duffy et al. | Feb 2000 | A |
6037812 | Gaudet | Mar 2000 | A |
6122336 | Anderson | Sep 2000 | A |
6307906 | Tanji et al. | Oct 2001 | B1 |
6316987 | Dally et al. | Nov 2001 | B1 |
6380783 | Chao et al. | Apr 2002 | B1 |
6389091 | Yamaguchi et al. | May 2002 | B1 |
6426660 | Ho et al. | Jul 2002 | B1 |
6507544 | Ma et al. | Jan 2003 | B1 |
6509773 | Buchwald et al. | Jan 2003 | B2 |
6650699 | Tierno | Nov 2003 | B1 |
6717478 | Kim et al. | Apr 2004 | B1 |
6838951 | Nieri et al. | Jan 2005 | B1 |
6917762 | Kim | Jul 2005 | B2 |
7078978 | Wakii | Jul 2006 | B2 |
7102449 | Mohan | Sep 2006 | B1 |
7142865 | Tsai et al. | Nov 2006 | B2 |
7158441 | Okamura | Jan 2007 | B2 |
7164631 | Tateishi et al. | Jan 2007 | B2 |
7199728 | Dally et al. | Apr 2007 | B2 |
7336112 | Sha et al. | Feb 2008 | B1 |
7336749 | Garlepp | Feb 2008 | B2 |
7532697 | Sidiropoulos et al. | May 2009 | B1 |
7535957 | Ozawa et al. | May 2009 | B2 |
7616075 | Kushiyama | Nov 2009 | B2 |
7650525 | Chang et al. | Jan 2010 | B1 |
7688887 | Gupta et al. | Mar 2010 | B2 |
7688929 | Co | Mar 2010 | B2 |
7697647 | McShea | Apr 2010 | B1 |
7822113 | Tonietto et al. | Oct 2010 | B2 |
7839229 | Nakamura et al. | Nov 2010 | B2 |
7852109 | Chan et al. | Dec 2010 | B1 |
7860190 | Feller | Dec 2010 | B2 |
7876866 | McAdam et al. | Jan 2011 | B1 |
8036300 | Evans et al. | Oct 2011 | B2 |
8045608 | Dai et al. | Oct 2011 | B2 |
8074126 | Qian et al. | Dec 2011 | B1 |
8116409 | Warner | Feb 2012 | B1 |
8161431 | Buonpane et al. | Apr 2012 | B2 |
8253454 | Lin | Aug 2012 | B2 |
8310389 | Chui et al. | Nov 2012 | B1 |
8370654 | Hasko et al. | Feb 2013 | B1 |
8407511 | Mobin et al. | Mar 2013 | B2 |
8443223 | Abbasfar | May 2013 | B2 |
8583072 | Ciubotaru et al. | Nov 2013 | B1 |
8649476 | Malipatil | Feb 2014 | B2 |
8744012 | Ding et al. | Jun 2014 | B1 |
8791735 | Shibasaki | Jul 2014 | B1 |
8898504 | Baumgartner et al. | Nov 2014 | B2 |
8929496 | Lee et al. | Jan 2015 | B2 |
8934594 | Malhotra | Jan 2015 | B1 |
9036764 | Hossain et al. | May 2015 | B1 |
9059816 | Simpson et al. | Jun 2015 | B1 |
9100232 | Hormati et al. | Aug 2015 | B1 |
9223327 | Zhu et al. | Dec 2015 | B1 |
9288089 | Cronie et al. | Mar 2016 | B2 |
9300503 | Holden et al. | Mar 2016 | B1 |
9306621 | Zhang et al. | Apr 2016 | B2 |
9374250 | Musah et al. | Jun 2016 | B1 |
9397868 | Hossain et al. | Jul 2016 | B1 |
9438409 | Liao et al. | Sep 2016 | B1 |
9444588 | Katie | Sep 2016 | B1 |
9520883 | Shibasaki | Dec 2016 | B2 |
9565036 | Zerbe et al. | Feb 2017 | B2 |
9577815 | Simpson et al. | Feb 2017 | B1 |
9602111 | Shen et al. | Mar 2017 | B1 |
9906358 | Tajalli | Feb 2018 | B1 |
9917607 | Zhang et al. | Mar 2018 | B1 |
9960902 | Lin et al. | May 2018 | B1 |
10055372 | Shokrollahi | Aug 2018 | B2 |
10193716 | Hormati et al. | Jan 2019 | B2 |
10326435 | Arp et al. | Jun 2019 | B2 |
10326623 | Tajalli | Jun 2019 | B1 |
10491365 | Lin | Nov 2019 | B1 |
10574487 | Hormati | Feb 2020 | B1 |
10791009 | Wu et al. | Sep 2020 | B1 |
10848351 | Hormati | Nov 2020 | B2 |
10892726 | Principe et al. | Jan 2021 | B2 |
10904046 | Hormati | Jan 2021 | B2 |
20030001557 | Pisipaty | Jan 2003 | A1 |
20030146783 | Bandy et al. | Aug 2003 | A1 |
20030212930 | Aung et al. | Nov 2003 | A1 |
20030214977 | Kuo | Nov 2003 | A1 |
20040092240 | Hayashi | May 2004 | A1 |
20040141567 | Yang et al. | Jul 2004 | A1 |
20050024117 | Kubo et al. | Feb 2005 | A1 |
20050078712 | Voutilainen | Apr 2005 | A1 |
20050084050 | Cheung et al. | Apr 2005 | A1 |
20050117404 | Savoj | Jun 2005 | A1 |
20050128018 | Meltzer | Jun 2005 | A1 |
20050195000 | Parker et al. | Sep 2005 | A1 |
20050201491 | Wei | Sep 2005 | A1 |
20050220182 | Kuwata | Oct 2005 | A1 |
20050275470 | Choi | Dec 2005 | A1 |
20060008041 | Kim et al. | Jan 2006 | A1 |
20060062058 | Lin | Mar 2006 | A1 |
20060140324 | Casper et al. | Jun 2006 | A1 |
20060232461 | Felder | Oct 2006 | A1 |
20070001713 | Lin | Jan 2007 | A1 |
20070001723 | Lin | Jan 2007 | A1 |
20070047689 | Menolfi et al. | Mar 2007 | A1 |
20070058768 | Werner | Mar 2007 | A1 |
20070086267 | Kwak | Apr 2007 | A1 |
20070110148 | Momtaz et al. | May 2007 | A1 |
20070127612 | Lee et al. | Jun 2007 | A1 |
20070146088 | Arai et al. | Jun 2007 | A1 |
20070147559 | Lapointe | Jun 2007 | A1 |
20070183552 | Sanders et al. | Aug 2007 | A1 |
20070201597 | He et al. | Aug 2007 | A1 |
20070253475 | Palmer | Nov 2007 | A1 |
20080007367 | Kim | Jan 2008 | A1 |
20080069276 | Wong et al. | Mar 2008 | A1 |
20080111634 | Min | May 2008 | A1 |
20080136479 | You et al. | Jun 2008 | A1 |
20080165841 | Wall et al. | Jul 2008 | A1 |
20080181289 | Moll | Jul 2008 | A1 |
20080219399 | Nary | Sep 2008 | A1 |
20080297133 | Duan et al. | Dec 2008 | A1 |
20080317188 | Staszewski et al. | Dec 2008 | A1 |
20090103675 | Yousefi et al. | Apr 2009 | A1 |
20090167389 | Reis | Jul 2009 | A1 |
20090195281 | Tamura et al. | Aug 2009 | A1 |
20090231006 | Jang et al. | Sep 2009 | A1 |
20090243679 | Smith et al. | Oct 2009 | A1 |
20090262876 | Arima et al. | Oct 2009 | A1 |
20090262877 | Shi et al. | Oct 2009 | A1 |
20100033259 | Miyashita | Feb 2010 | A1 |
20100090723 | Nedovic et al. | Apr 2010 | A1 |
20100090735 | Cho | Apr 2010 | A1 |
20100156543 | Dubey | Jun 2010 | A1 |
20100177816 | Malipatil et al. | Jul 2010 | A1 |
20100180143 | Ware et al. | Jul 2010 | A1 |
20100220828 | Fuller et al. | Sep 2010 | A1 |
20100253314 | Bitting | Oct 2010 | A1 |
20100283894 | Horan et al. | Nov 2010 | A1 |
20100329322 | Mobin et al. | Dec 2010 | A1 |
20100329325 | Mobin et al. | Dec 2010 | A1 |
20110002181 | Wang et al. | Jan 2011 | A1 |
20110025392 | Wu et al. | Feb 2011 | A1 |
20110148498 | Mosalikanti et al. | Jun 2011 | A1 |
20110234278 | Seo | Sep 2011 | A1 |
20110311008 | Slezak et al. | Dec 2011 | A1 |
20120051480 | Usugi et al. | Mar 2012 | A1 |
20120146672 | Winter et al. | Jun 2012 | A1 |
20120170621 | Tracy et al. | Jul 2012 | A1 |
20120200364 | Iizuka et al. | Aug 2012 | A1 |
20120206177 | Colinet et al. | Aug 2012 | A1 |
20120235717 | Hirai et al. | Sep 2012 | A1 |
20120288043 | Chen et al. | Nov 2012 | A1 |
20120293195 | Bourstein | Nov 2012 | A1 |
20120327993 | Palmer | Dec 2012 | A1 |
20130088274 | Gu | Apr 2013 | A1 |
20130091392 | Valliappan et al. | Apr 2013 | A1 |
20130093471 | Cho et al. | Apr 2013 | A1 |
20130107997 | Chen | May 2013 | A1 |
20130108001 | Chang et al. | May 2013 | A1 |
20130114519 | Gaal et al. | May 2013 | A1 |
20130207706 | Yanagisawa | Aug 2013 | A1 |
20130243127 | Ito et al. | Sep 2013 | A1 |
20130264871 | Zerbe et al. | Oct 2013 | A1 |
20130271194 | Madoglio et al. | Oct 2013 | A1 |
20130285720 | Jibry | Oct 2013 | A1 |
20130314142 | Tamura et al. | Nov 2013 | A1 |
20130315290 | Farjad-Rad | Nov 2013 | A1 |
20140086291 | Asmanis et al. | Mar 2014 | A1 |
20140286381 | Shibasaki | Sep 2014 | A1 |
20140286457 | Chaivipas | Sep 2014 | A1 |
20140376604 | Verlinden et al. | Dec 2014 | A1 |
20150043627 | Kang et al. | Feb 2015 | A1 |
20150078495 | Hossain et al. | Mar 2015 | A1 |
20150117579 | Shibasaki | Apr 2015 | A1 |
20150180642 | Hsieh et al. | Jun 2015 | A1 |
20150220472 | Sengoku | Aug 2015 | A1 |
20150256326 | Simpson et al. | Sep 2015 | A1 |
20160056980 | Wang et al. | Feb 2016 | A1 |
20160087610 | Hata | Mar 2016 | A1 |
20160134267 | Adachi | May 2016 | A1 |
20170005785 | Aleksic et al. | Jan 2017 | A1 |
20170060221 | Yu et al. | Mar 2017 | A1 |
20170134190 | Hoshyar et al. | May 2017 | A1 |
20170228215 | Chatwin et al. | Aug 2017 | A1 |
20170244371 | Turker Melek et al. | Aug 2017 | A1 |
20170310456 | Tajalli | Oct 2017 | A1 |
20180083638 | Tajalli | Mar 2018 | A1 |
20180083763 | Black et al. | Mar 2018 | A1 |
20180219539 | Arp et al. | Aug 2018 | A1 |
20180227114 | Rahman et al. | Aug 2018 | A1 |
20180248723 | Palmer | Aug 2018 | A1 |
20180343011 | Tajalli et al. | Nov 2018 | A1 |
20180375693 | Zhou et al. | Dec 2018 | A1 |
20190109735 | Norimatsu | Apr 2019 | A1 |
20190377378 | Gharibdoust | Dec 2019 | A1 |
20200162233 | Lee et al. | May 2020 | A1 |
20210248103 | Khashaba et al. | Aug 2021 | A1 |
20210281449 | Wang et al. | Sep 2021 | A1 |
20220085967 | Vrazel | Mar 2022 | A1 |
Number | Date | Country |
---|---|---|
203675093 | Jun 2014 | CN |
0740423 | Oct 1996 | EP |
2451129 | May 2012 | EP |
3615692 | Nov 2004 | JP |
Entry |
---|
International Search Report and Written Opinion for PCT/US2018/065282, dated Mar. 29, 2019, 1-8 (8 pages). |
Choi, Kyu-Won , et al., “Hierarchical Power Optimization for System-on-a-Chip (SoC) through CMOS Technology Scaling*”, School of Electrical and Computer Engineering, Georgia Institute of Technology, 2002, 1-24 (24 pages). |
Lackey, David , et al., “Managing Power and Performance for System-on-Chip Designs using Voltage Islands”, IEEE/ACM International Conference on Computer-Aided Design, Nov. 10-14, 2002, Dec. 2002, 195-202 (8 pages). |
Riley, M. W. , et al., “Cell Broadband Engine Processor: Design and Implementation”, IBM Journal of Research and Development, vol. 51, No. 5, Sep. 2007, 545-557 (13 pages). |
Chang, Hong-Yeh , et al., “A Low-Jitter Low-Phase-Noise 10-GHz Sub-Harmonically Injection-Locked PLL With Self-Aligned DLL in 65-nm CMOS Technology”, IEEE Transactions on Microwave Theory and Techniques, vol. 62, No. 3, Mar. 2014, 543-555 (13 pages). |
Ha, J.C. , et al., “Unified All-Digital Duty-Cycle and phase correction circuit for QDR I/O interface”, Electronic Letters, The Institution of Engineering and Technology, vol. 44, No. 22, Oct. 23, 2008, 1300-1301 (2 pages). |
Loh, Mattew , et al., “A 3×9 Gb/s Shared, All-Digital CDR for High-Speed, High-Density I/O”, IEEE Journal of Solid-State Circuits, vol. 47, No. 3, Mar. 2012, 641-651 (11 pages). |
Nandwana, Romesh Kumar, et al., “A Calibration-Free Fractional-N Ring PLL Using Hybrid Phase/Current-Mode Phase Interpolation Method”, IEEE Journal of Solid-State Circuits, vol. 50, No. 4, Apr. 2015, 882-895 (14 pages). |
Ng, Herman Jalli, et al., “Low Phase Noise 77-GHz Fractional-N PLL with DLL-based Reference Frequency Multiplier for FMCW Radars”, European Microwave Integrated Circuits Conference, Oct. 10-11, 2011, 196-199 (4 pages). |
Ryu, Kyungho , et al., “Process-Variation-Calibrated Multiphase Delay Locked Loop With a Loop-Enbedded Duty Cycle Corrector”, IEEE Transactions on Circuits and Systems, vol. 61, No. 1, Jan. 2014, 1-5 (5 pages). |
Tajalli, Armin , “Wideband PLL Using Matrix Phase Comparator”, Journal of Latex Class Files, vol. 14, No. 8, Aug. 2016, 1-8 (8 pages). |
Tan, Han-Yuan , “Design of Noise-Robust Clock and Data Recovery Using an Adaptive-Bandwidth Mixed PLL/DLL”, Harvard University Thesis, Nov. 2006, 1-169 (169 pages). |
Wang, Yi-Ming , et al., “Range Unlimited Delay-Interleaving and -Recycling Clock Skew Compensation and Duty-Cycle Correction Circuit”, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 23, No. 5, May 2015, 856-868 (13 pages). |
Won, Hyosup , et al., “A 28-Gb/s Receiver With Self-contained Adaptive Equalization and Sampling Point Control Using Stochastic Sigma-Tracking Eye-Opening Monitor”, IEEE Transactions on Circuits and Systems-I: Regular Papers, vol. 64, No. 3, Mar. 2017, 664-674 (11 pages). |
International Search Report and Written Opinion for PCT/US2022/026776, dated Jul. 21, 2022, 1-11 (11 pages). |
Cui, Delong , et al., “A Dual-Channel 23-Gbps CMOS Transmitter/Receiver Chipset for 40-Gbps RZ-DQPSK and CS-RZ-DQPSK Optical Transmission”, IEEE Journal of Solid-State Circuits, vol. 47, No. 12, Dec. 2012, 3249-3260 (12 pages). |
Inti, Rajesh , et al., “A 0.5-to-2.5 Gb/s Reference-Less Half-Rate Digital CDR with Unlimited Frequency Acquisition Range and Improved Input Duty-Cycle Error Tolerance”, IEEE Journal of Solid-State Circuits, vol. 46, No. 12, Dec. 2011, 3150-3162 (13 pages). |
Pozzoni, Massimo , et al., “A Multi-Standard 1.5 to 10 Gb/s Latch-Based 3-Tap DFE Receiver with a SSC Tolerant CDR for Serial Backplane Communication”, IEEE Journal of Solid-State Circuits, vol. 44, No. 4, Apr. 2009, 1306-1315 (10 pages). |
Shu, Guanghua, el al., “A 4-to-10.5 Gb/s Continuous-Rate Digital Clock and Data Recovery With Automatic Frequency Acquisition”, IEEE Journal of Solid-State Circuits, vol. 51, No. 2, Feb. 2016, 428-439 (12 pages). |
Yoo, Danny , et al., “A 36-GB/s Adaptive Baud-Rate CDR with CTLE and 1-Tap DFE in 28-nm CMOS”, IEEE Solid-State Circuits Letters, vol. 2, No. 11, Nov. 2019, 252-255 (4 pages). |
Zaki, Ahmed M., “Adaptive Clock and Data Recovery for Asymmetric Triangular Frequency Modulation Profile”, IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), Aug. 21, 2019, 1-6 (6 pages). |
Number | Date | Country | |
---|---|---|---|
20220329463 A1 | Oct 2022 | US |