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Published byJason Walsh Modified over 3 years ago

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1 Practical Macromodels for Digital I/O Paul Franzon Madhavan Swaminathan, Michael Steer Acknowledgements: Ambrish Varma, Bhyrav Mutnury

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2 Outline Background –Macromodeling Needs –Requirements for Successful Macromodels Macromodeling Techniques –IBIS –Numerical Models –Physical Proposal : The Way Forward

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3 I/O Macromodeling Replace full Spice driver model with Macromodel –Hides proprietary information –Speeds up simulation

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4 I/O Macromodeling Required Goals: –Vendor-independent format –Have to capture TX, RX, and package parasitic essentials –Sufficiently accurate to be useful –Easy to automatically generate from Spice and Measurements –Easy to verify –Easy and fast to simulate

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5 I/O Macromodels Desirable Goals –Unique solution –Orthogonality –Multiple Verification Approaches –Physical basis –Human readability –Monotonic –Simulator Independent –Extendable to core noise SSN

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6 IBIS –Input Output Buffer Information Specification –EIA Standard 656 -A –Behavioral model of I/O buffers Model is represented as set of VI and VT curves. –Fast simulations –Protects proprietary information contained in the IC

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7 Basic IBIS Model A Basic model consist of: Four I-V curves: - pullup and pulldown - PWR and GND Clamp Two Ramps:-dv/dt_rise, dv/dt_fall Die Capacitance and Packaging information

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8 Spice2Ibis Automatically converts a Spice deck to an IBIS file Over 1,000 active users Over 3,000 lines of code Was critical to success of IBIS language S2IBIS Command File -Header information -Component Description Parser -Extracts all relevant information from Command file SPICE -HSPICE,PSPICE, SPICE2 SPICE3,SPECTRE S2IBIS - Calls Spice, analyzes data Prints results IBIS Model Circuit Layout

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9 IBIS Limitations IBIS hard to scale for use with high speed I/O –Up to 100 ps delay error –Subtle inflections missed –SSN over-predicted

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10 IBIS Alternatives Current Work: Numerical black-box methods –Parametric Models: Stievano, Maio, and Canavero, Politecnico di Torino –Spline/Finite Time Difference Swaminathan, Georgia Tech

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11 Numerical Methods Black Box Modeling –Static Term i static (k) = w 1 (k)f 1 (k) + w 2 (k)f 2 (k) Captures output impedance of driver IBIS: –f1 and f2 are fitted VI tables. i.e. I(Vout) Spline : –f1 and f2 are numerically fitted power series, i.e. I(Vout, ….) Parametric: –Basis functions: Gaussian or Sigmoid

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12 Numerical Methods Black Box Modeling –Dynamic Term IBIS : i(k) = w 1 (k)V 1 (k)f 1 (k) + w 2 (k)V 2 (k)f 2 (k) –+ output physical Ccomp Spline : –f1, f2 dynamic by numerically fitted load capacitance (captures di/dt) Parametric –Calculated as a function of past values, using Basis functions –e.g. RBF using Gaussians V n (T)

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13 Physical Model Circuit Level: or M 1 (T) M 2 (T) Ids=k(Vgs-Vt) 2 G S D etc. Captures staged turn-on drivers Gain loss during switching event (Vgs) Second order effects = f(Vds) w=step function only in break before make drivers CML reduces CM dependence

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14 Comments on Models IBIS –No numerical flexibility to capture subtle physical effects –Most physical (but very first order) –Easy to automate Spline/FTD –Input waveform dependence –Less physical –More accurate –Relatively hard to automate Numerical fitting of power series Parametric –Input waveform dependence –Least physical –Most accurate –Harder to automate model production More complex numerical procedures

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15 Proposal Collaborative Effort: –NC State University –Georgia Tech –Politecnico di Torino

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16 The Way Forward 1.Find the compromise between complexity and automation, while considering other goals IBIS 4.0 SplineRBF I=f(Vout) ErrorCurrentSource* I=f(Vout,Vdd/Vss) M=f(T,Vdd) Etc. *e.g. Numerically fitted error fn RBF Sigmoid Power Series Reduced Order Power Series Wavelet basis fns Neural net fitting Etc.

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17 The Way Forward Test on standard driver set: –Conventional, LVDS, DDR, Other CML, Emerging –Test Spice model formats in Freeda Evaluate –Metrics: Accuracy in predicting delay, peak SSN, xmitted SSN, Xtalk, refn noise Accuracy outside range where fitted Macromodel utility factors listed earlier –Esp. Complexity of model fit procedure

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18 The Way Forward 2. Promulgate alpha standard –Develop SpicetoMacromodel 3. Evaluate and propagate more broadly

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19 Proposed Consortium Set up industry consortium to fund this work Benefits to consortium members –IC vendor companies Macromodel tuned to their drivers Early access to successful macromodel formats –First to market –CAD companies Early access to successful formats Influence macromodel for simulator compatibility

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20 Discussion (on the day) Acknowledgement and contribution of the highly accurate black box techniques contributed by Prof. Flavio Canavero and his group. George Katopis: 1. Study and development of a tool that helps the user with the selection of black box option based on "expected" accuracy and time. 2. Automatic generation of the black box models

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21 Conclusions Ease of Use is just as important as model accuracy All macromodels are numerical black box format –Key question is complexity and type of functions used

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