Presenters. Time Domain and Statistical Model Development, Simulation and Correlation Methods for High Speed SerDes

Transcription

1 JANUARY 28-31, 2013 SANTA CLARA CONVENTION CENTER Time Domain and Statistical Model Development, Simulation and Correlation Methods or High Speed SerDes Presenters Xingdong Dai Fangyi Rao Shiva Prasad Kotagiri John Baprawski Cathy Ye Liu LSI Corporation Agilent Technologies LSI Corporation Consultant LSI Corporation 1

2 Agenda Overview o I/O Buer Inormation Speciication (IBIS) and Algorithmic Modeling Interace (AMI) Time domain model development Rapid prototyping o a statistical model Simulation and jitter methods in time domain and statistical domain Correlations between simulations in time domain and statistical domain Conclusion and uture works IBIS-AMI AMI Incorporated in IBIS 5.0 (2008), updated in 5.1 (2012) Many additional model and simulation proposals Designed to support SerDes modeling eorts Electrical (analog) and algorithmic models 2

3 Dual model and simulation lows AMI Functions AMI_Init(), AMI_GetWave() and AMI_Close() Statistical Impulse response based signal processing through AMI_Init(). LTI Fastest simulation speed Arbitrary low BER loor Not based on any particular bit pattern (8B10B constraints are applied when needed) Time domain / bit accurate / bit by bit Waveorm based signal processing through AMI_GetWave(). NLTV Faster simulation speed than SPICE Accurate modeling o DFE, CDR and adaptation processes Stimulus required EDA tool-level extrapolation required to low BER loor Agenda Overview o I/O Buer Inormation Speciication (IBIS) and Algorithmic Modeling Interace (AMI) Time domain model development Rapid prototyping o a statistical model Simulation and jitter methods in time domain and statistical domain Correlations between simulations in time domain and statistical domain Conclusion and uture works 3

4 Time domain modeling method Analog ront end AFE components Variable gain ampliier (VGA) or automatic gain control (AGC) Continuous time linear equalizer (CTLE) Linear and non-linear AFE ilter construction Linear Obtain AC transer unction across PVT Filter itting in S or Z domain S-domain: convolution based, require larger memory buer Z-domain: more eicient (speed and memory), but coeicients need update at each sampling requency Non-linear Based on hyperbolic tangent unction or polynomial unction 4

5 Analog ront end (continued) Decision eedback equalizer Modeled as a series o FIR ilter Range and resolution are obtained rom circuit simulations Finite rise and all time Modeled with exponential pulse shaping unction DFE architecture conormity to IBIS-AMI 2T is interleaved and mapped to single bit stream 5

6 Clock data recovery CDR behaviors are observed in clock_times vector Do note that clock_times is speciied exactly 0.5 UI beore data sample CDR can be bypassed with -1 value EDA tool re-construct signal eye diagram Watch out or bit period rounding errors or mismatches between model and EDA simulator Adaptation loops Conigure RX to produce optimal results, given an input signal RX may have multiple interacting adaptation loops Adaptation time is conservatively estimated in Ignore_Bits reserved parameter Input signal characteristics Algorithm such as DFE loating tap search Implementation oten involves multi-stages o digital accumulators Obtain time average o adaptation gradient Delay/size o these accumulators can be eicient evaluated in IBIS-AMI than SPICE or AMS based approach 6

7 Agenda Overview o I/O Buer Inormation Speciication (IBIS) and Algorithmic Modeling Interace (AMI) Time domain model development Rapid prototyping o a statistical model Simulation and jitter methods in time domain and statistical domain Correlations between simulations in time domain and statistical domain Conclusion and uture works Rapid prototyping method or statistical model It is not easible to capture entire dynamic behaviors o a bit accurate model in statistical domain. It is possible to approximate a bit accurate model at one instant where behaviors can be considered static. Accomplished by cascading ideal ilter and distortion ilter, where distortion ilter strives to match NLTV model behaviors o the time domain model Such an instant is called a state or an operating condition. A collection o states, a state space. 7

8 State space Bit accurate model behaviors are inluenced by many actors External: TX settings, interconnect, bit pattern, bit rate Internal: adaptation algorithm, convergence rate, run length Deine a reasonable size o state space or statistical model development Ininite states in a bit accurate model Size o state space deine modeling eorts and accuracy Structure o statistical model 8

9 Statistical model implementation Ideal ilter Coeicients can be derived rom impulse response matrix input rom EDA; Or Based on LTI implementation o AFE ilters Distortion ilter Deinition o correlation criteria May include IIR (tunable pole/zero), gain, FFE Channel identiication and ilter selection Set up distortion ilter LUT or parameters based on characterized states Parameter interpolation may be required Agenda Overview o I/O Buer Inormation Speciication (IBIS) and Algorithmic Modeling Interace (AMI) Time domain model development Rapid prototyping o a statistical model Simulation and jitter methods in time domain and statistical domain Correlations between simulations in time domain and statistical domain Conclusion and uture works 9

10 Analog channel characterization Analog channel characterization by impulse response Common to both time domain and statistical simulation lows Pre-characterized with SPICE prior to AMI simulation For XT, ull size S-parameter channel model is recommended TX (n) RX (n) )... )... TX (m) ) Multi-port S-parameters RX (m) ) Jitter methods IBIS 5.0 and 5.1 deine a limited set o jitter Tx_Jitter, Tx_DCD, Rx_Clock_PDF More jitter types and noise are proposed in IBIS BIRD 123 Permits iner grain modeling and silicon correlation Supported at tool level on selected EDA simulators r DJ r DJ RJ r RJ Acos[ n Acos[ n r T ] ( 1) T ] ( 1) n r n ( i 2 ) 2 10

11 Time domain (bit by bit) simulation AMI low SPICE TX AMI_Init RX AMI_Init Channel Imp Char. (EDA) h AC Apply LTI EQ to Imp (SerDes) h TxAC Model init (SerDes) RX input v RxIn ( n) h b n TxAC ( n) convolution RX input Waveorm Gen (EDA) RX AMI_GetWave RX NLTV EQ (SerDes) Compute eye & BER (EDA) b (n) TX (n)... htxac (n) b (n) v RxIn RX GetWave RX out b (m) h TxAC (m) b (m) TX (m) Time domain (bit by bit) jitter TX jitter TX jitter modiies transition edge o signal waveorms Jitter ampliication is considered when convolving with channel impulse response RX Jitter CDR jitter captured rom clock_times rom AMI_GetWave() Applied to eye diagram and BER calculation Extrapolation algorithm is used to reach low BER loor Implementation is proprietary 11

12 Statistical simulation and jitter AMI low Impulse response RX AMI_Init returns combined impulse that represents TX EQ, channel and RX EQ Length determines the total number o bit patterns Jitter handling: same as in time domain low TX jitters are applied to transitions in stimulus and ampliied by channel loss RX jitters are added to calculated probability densities in post-processing Eye and BER are calculated based on probability density unction p( v, t) 1 2 d 2 0 ( m) r 1 d 2 RJ M 1 m d ( m) [ v v RJ ( m) d ( t)] ( m) r DJ i ( m) RJ ( m) RJ g[ r ] g[ ] ( m) DJ ( m) DJ d[ r ] d[ ] d ( m) DJ Agenda Overview o I/O Buer Inormation Speciication (IBIS) and Algorithmic Modeling Interace (AMI) Time domain model development Rapid prototyping o a statistical model Simulation and jitter methods in time domain and statistical domain Correlations between simulations in time domain and statistical domain Conclusion and uture works 12

13 Correlation criteria election No single parameter can deine simulation result A set o parameters are elected as correlation criteria Iterations are needed to achieve close correlation Set size determines the eort level Signal waveorm Rise time, all time, high level, low level, Eye diagram Inner eye height, inner eye width, crossing width, Correlation criteria election (cont d) Correlation criteria 1 Distortion Filter IIR Correlation Criteria Signal-to-Noise Ratio (SNR) Correlation criteria 2 Criteria Function Measure the ratio o eye amplitude and the sum o standard deviations o the logic-1 and logic-0 histograms. Gain Level 1 Measure mean value o logic-1 level across the eye level boundary. Distortion Correlation Criteria Function Filter Criteria IIR Rise Time The average time rom low to high amplitude thresholds. Gain Level 1 Measure mean value o logic-1 level across the eye level boundary. 13

14 Model correlation at 12Gbs Measurement Time Domain Statistical Level mv 165 mv Level mv -165 mv Amplitude 330 mv 330 mv Height 199 mv 185 mv Width ps ps SNR Rise Time ps ps Fall Time ps ps Jitter (RMS) ps ps Model correlation at 3Gbs Measurement Time Domain Statistical Level mv 207 mv Level mv -207 mv Amplitude 412 mv 414 mv Height 373 mv 402 mv Width ps ps SNR Rise Time ps ps Fall Time ps ps Jitter (RMS) ps ps 14

15 Agenda Overview o I/O Buer Inormation Speciication (IBIS) and Algorithmic Modeling Interace (AMI) Time domain model development Rapid prototyping o a statistical model Simulation and jitter methods in time domain and statistical domain Correlations between simulations in time domain and statistical domain Conclusion and uture works Conclusion and uture work A rapid prototyping method is presented to support IBIS-AMI dual modeling and simulation low Permits time domain model closest matching to silicon implementation Enables ast turn around o statistical model based on deined state space Good correlations can be achieved on selected criteria Future work Enhancing statistical model with additional criteria or eort level Veriy correlation results in cross talk conigurations 15

16 DesignCon 13 Track-8TA3 16