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TCAD for compact modeling

TCAD for compact modeling . Luca Sponton , Paul Pfaeffli and Lars Bomholt. Outline. Introduction Bringing process information to design PCM example Process-aware SPICE compact models Challenges for TCAD-generated models Summary. Motivation.

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TCAD for compact modeling

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  1. TCAD for compact modeling Luca Sponton, Paul Pfaeffli and Lars Bomholt

  2. Outline • Introduction • Bringing process information to design • PCM example • Process-aware SPICE compact models • Challenges for TCAD-generated models • Summary

  3. Motivation • The impact of variation on product performance and yield grows • Increasing difficulty in keeping strict statistical control on a process • Be able to design robustness against process variation into the product • Be able to optimize manufacturing for product performance Bring process variations to circuit simulation and design

  4. Variations Lead to Yield Loss • Parametric yield loss is caused by variations: • Increasing margins would substantially diminish the advantages of technology scaling. • Corner models could bring to a too conservative design • Corner models do not allow any understanding of the process variation impact on a design Both design and process may contribute to the deviations.

  5. Outline • Introduction • Bringing process information to design • PCM example • Process-aware SPICE compact models • Challenges for TCAD-generated models • Summary

  6. PROS: • Well established methodology • Automatic procedure • Captures most of variability • CONS: • Components are not correlated with underlying process variations • Overestimates • Parameters can assume unphysical values • Stable process flow Traditional flow: data from measurements E-TEST DATA SPICE PARAMETER EXTRACTION CORRELATION ANALYSIS PRINCIPAL COMPONENT ANALYSIS GENERATE EQUATIONS MONTECARLO SIMULATION

  7. How do we bring process information into design? Advantages of TCAD extracted models • TCAD is the ideal tool to characterize process variations • TCAD simulations are accurate, do not drift during time and are available in an early stage of technology development • It is possible to access process parameters and device characteristics that are not controlled or measured accurately in manufacturing • It is cheaper than running large design of experiments on silicon

  8. Process simulation • - DOE of process variations - Device simulations Bringing process to circuit simulation with TCAD Process variability Device characteristics Compact models Full circuit

  9. Process compact models (PCM) • PCM creates a link between the space of process variables and device characteristics through a response surface model Device Characteristic = f(Process Characteristics) • Let us try to do the same for SPICE parameters: Vth0=f1(Tox, Ch_Dose, Ha_dose, Spike_T, …) u0=f2(Tox, Ch_Dose, Ha_dose, Spike_T, …) … • Physically meaningful parameters, that vary smoothly with process variations, allow for a better quality of PCM

  10. Process compact model for SPICE parameters Process variables {Pi} Set of Process variables {P} DOE, n experiments Process simulation n devices PCM PCM generation Device simulations SPICE parameters extraction SPICE model card N SPICE model cards

  11. Consistent extraction from TCAD • Nominal SPICE model obtained from nominal process • Small subset of parameters extracted to take into account process variability • Automatic BSIM3 SPICE parameters extraction for every device in the DOE • Accurate models obtained by a combined local optimization + global refinement • Use of bounded optimization to ensure physical meaning of extracted parameters Extraction of Nominal device Selection of Parameters subset Parameters extraction for whole DOE PCM extraction Use Local Optimization

  12. Outline • Introduction • Bringing process information to design • PCM example • Process-aware SPICE compact models • Challenges for TCAD-generated models • Summary

  13. PCM Example • Full factorial DOE on gate length, gate oxide thickness, Halo implant dose & tilt and channel dose • Nominal device extracted, then a subset of 16 SPICE parameters is used to account for process variations: vth0, Ua, Uc, k1, k2, Voff, Nfactor, eta0,Delta, Vsat, a0, Pclm, pdiblc1, Keta • Computational time is ~ 3h for each process variation. Experiments can be parallelized on a cluster of computers

  14. Consistent extraction • Extracted parameters follow process variations thanks to the local optimization and bounded extraction algorithm

  15. Consistent extraction • Physical meaning of parameters is maintained by the extraction strategy chosen, accuracy may suffer compared to global optimization due to the limited set of parameters chosen • From the full set of SPICE cards a PCM is generated

  16. PCM generation • PCM generated SPICE model accuracy is good using neural networks as response surfaces. Simple polynomial response surface models do not give a good accuracy.

  17. PCM generated SPICE parameters • Predicted models show some error when compared to TCAD simulations: we trade some accuracy for getting the process-design link

  18. Consistent extraction from TCAD • PROS: • Early availability of models • Physically meaningful SPICE parameters • Consistent extraction and PCM allow linking directly SPICE parameters to process variations • CONS: • Nominal device extraction time consuming • Automated extraction flow has to be optimized on the process • Accuracy worse than with global ‘unbounded’ optimization • Model prediction introduces additional error

  19. Outline • Introduction • Process variations to circuit variations • PCM example • Process-aware SPICE compact models • Challenges for TCAD-generated models • Summary

  20. Process-aware SPICE models: PARAMOS • Different approach: embedding process parameters directly into the SPICE model • Extraction of SPICE parameters including PCM parameters through extraction from an entire DOE reflecting the process conditions • Parameter definition: With Mi SPICE parameter, Pi process parameter • Extraction tool: Paramos

  21. Process-Aware SPICE Model: PARAMOS • TCAD simulations • Generate I-V / C-V database for each process parameter set {Pi} • Global SPICE extraction • Create a compact model with process parameters as SPICE library parameters. • Polynomial fitting • Vth = Vth0 + SSai(n)Pin • Voff = Voff0 + SSbi(n)Pin • All curves and coefficients are extracted in a single optimization step Manufacturing Calibration TCAD (process & device) { Pi } I-V, C-V database {Pi} SPICE Extraction Process-Aware Compact SPICE Model{Pi} {Pi} accessible for circuit simulations! Courtesy of S. Tirumala, Synopsys

  22. Case Study • Typical 90 nm Technology • Tox = 16 A, Lg = 65 nm, Vdd = 1.0 V • Normalized variation Dpi: Dpi = (Pi - Pi0)/(Pimax - Pi0) Range of Dpi : from -100% to +100% Courtesy of S. Tirumala, Synopsys

  23. Model TCAD C-V Model TCAD sim Quality of Compact Model Extraction • Excellent fit for I-V curves (rms error < 5%) • Excellent fit for Vt-Lin and Idsat ( <4%) • Acceptable fit for Ioff (<40%) Courtesy of S. Tirumala, Synopsys

  24. Input rise Delay Variation and Sensitivity • Delay is most sensitive to Tox variation . • Varies from -10% to +24% as Dpox changes -100% to +100% • The response to gate length variation (DL) is relatively weak • - 5% to +5% across the full range of DL variation. • Variation around nominal process is non-symmetrical • -10 % vs 24% for min and max variation. Courtesy of S. Tirumala, Synopsys

  25. Outline • Introduction • Process variations to circuit variations • Extraction techniques for variability • Process-aware SPICE compact models • Challenges for TCAD-generated models • Summary

  26. Challenges for TCAD-generated models • There are historically some missing links between TCAD and compact model extraction [1]: • Lithography effects on gate shape • Isolation formation (STI or LOCOS) • Outdiffusion of implanted dopant • … • All these effects are 3D effects not easily accountable for with 2D simulations [1] C.C. McAndrews, “Predictive technology characterization, missing links between TCAD and compact modeling”, Proc. of SISPAD, 2000

  27. Bridging the gap: lithography Lithography effects on gate shape: L.Sponton et al., “A Full 3D TCAD Simulation Study of Line-Width Roughness Effects in 65 nm Technology”, Proc. Of SISPAD, Sept. 2006

  28. Bridging the gap: INWE Narrow width effect on the transistor characteristics

  29. Summary • There is a growing need to understand the effect of process variation on circuit performance • Using calibrated TCAD simulations it is possible to study the effects of slight process variation on device characteristics • Process-aware SPICE models offer a way to bring process variation information to the design sphere • With standard BSIM models it is necessary to trade some accuracy for being able to properly and consistently consider these variations

  30. Acknowledgements • The authors would like to thank people at Synopsys and ETH Zurich for their contribution to the work, in particular Dipankar Pramanik, Shridhar Tirumala, Sathya Krishnamurthy, Yuri Mahotin • Part of this work was financed through the KTI Project “Parametric Design and Analysis for Semiconductor Technology Computer Aided Design (PARA-TCAD)”

  31. Thanks for your attention Luca SpontonSwiss Federal Institute of Technology (ETH)Integrated Systems LaboratoryPhone: +41  44 632 7786 (ETH)Phone: +41 44 567 1555 (SYNOPSYS)Email:  luca@iis.ee.ethz.ch

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