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Task 2.2 Update

Task 2.2 Update. 5 th October 2011 Agrate, Milano. Contents. Deliverables Completed Deliverables Final Deliverable. Contents. Deliverables Completed Deliverables Final Deliverable. T2.2 Deliverables. T2.2 Deliverables. Contents. Deliverables Completed Deliverables

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Task 2.2 Update

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  1. Task 2.2 Update • 5th October 2011 • Agrate, Milano

  2. Contents • Deliverables • Completed Deliverables • Final Deliverable

  3. Contents • Deliverables • Completed Deliverables • Final Deliverable

  4. T2.2 Deliverables

  5. T2.2 Deliverables

  6. Contents • Deliverables • Completed Deliverables • Final Deliverable

  7. Previously Reported

  8. What was accomplished? Statistical Variability in 32nm Bulk CMOS Technology, and in Nanowires. Statistical Variability in DPD, SiC, GaN/AlGaN Technologies Compact Modelling Strategies for Statistical Variability T2.2 Deliverables

  9. D2.2.4: Statistical Variability in 32nm Bulk CMOS Technology UNGL Contribution

  10. D2.2.4: Statistical Variability in 32nm Bulk CMOS Technology IUNET Contribution

  11. D2.2.4: Statistical Variability in 32nm Bulk CMOS Technology RVT-NMOS RVT-PMOS RVT-NMOS SNPS Contribution RVT-PMOS

  12. D2.2.4: Statistical Variability in Nanowire technology IMEP Contribution

  13. PCM STUDIO EHD5 SEMICELL SENTAURUS WORKBENCH DOE PCM D2.2.4: Statistical Variability in DPD, SiC, GaN/AlGaN Technologies ST-I Contribution

  14. D2.2.4: Statistical Variability in DPD, SiC, GaN/AlGaN Technologies POLI Contribution

  15. D2.2.4: Compact Modelling Strategies for Statistical Variability UNGL Contribution RVT-PMOS RVT-NMOS

  16. D2.2.4: Drain Current Variability in 45nm Bulk N-MOSFET IMEP Contribution

  17. T2.2 Deliverables • What was accomplished? • UNGL: Creation and Study of Variability in 22nm FinFET • IUNET Contribution

  18. D2.2.5: UNGL: Variability in 22nm FinFET UNGL Contribution

  19. D2.2.5: IUNET Contribution Deliverable delayed but has been completed and submitted.

  20. Contents • Deliverables & Timeline • Completed Deliverables • Final Deliverable

  21. Final T2.2 Deliverable Plans and Initial Progress

  22. NMX contribution (in collab. with IUNET-MI)Task 2.2D2.2.6 Andrea Ghetti, Augusto Benvenuti

  23. Investigation of RDF and RTN depedence on Substrate Doping Investigated different doping profile varying along the length, width and depth of the device Doping engineering in the vertical direction most effective in reducing RDF RTN reduces more putting dopant atoms as far as possible from the interface

  24. T2.2 publication list Journals Gareth Roy, Andrea Ghetti, Augusto Benvenuti, Axel Erlebach, Asen Asenov, “Comparative Simulation Study of the Different Sources of Statistical Variability in Contemporary Floating Gate Non-Volatile Memory”, IEEE-TED, in press Workshops Conferences Proceedings A. Ghetti, S.M. Amoroso, A. Mauri, C. Monzio Compagnoni , "Doping Engineering for Random Telegraph Noise Suppression in Deca-nanometer Flash Memories“, International Memory Workshop 2011, p. 91, Monterey, CA; 5/22-25/2011

  25. SNPS ContributionT2.2D2.2.6

  26. Implementations

  27. Plan for Deliverable D2.2.6 • SNPS agreed to join D2.2.6. •  We plan to apply some of the new methods implemented inSdevice to the NVM structure. •  In detail we are thinking about the following: • Investigation of influence of single traps and single dopands on IV characteristicsand gate leakage (direct statistical method). • Applying IFM hybrid method to RDF.

  28. IUNET contribution for D2.2.6 Alessandro Spinelli – UMET-MI in collab. with NMX Susanna Reggiani – UNET-BO Paolo Pavan, Luca Larcher – UNET-MORE

  29. Study of RDF and RTN dependence on device geometry • Different curvature radii of the active area of template MOSFETs were considered • RDF VT distribution slightly widens for larger radii • RTN VT slope improves as curvature radius is increased

  30. MODERN Progress report: IUNET-Bologna Industrial Partner: MICRON D 2.2.6 – Proposed activity: Sensitivity analysis of Non Volatile Memory device performance as a function of random dopant fluctuations (RDF). Comparison of the RDF results carried out by using Sentaurus Device and (i) the Impedance Field Method (IFM), based on the Green’s function noise calculations, or (ii) a set of randomized doping configurations generated by using the “cloud-in-cell” method. • Status: on schedule • Next steps: • Investigation of the Vth of a 32-nm Flash cell (template device) • Determination of the role played by the doping definition (see right figure). • Determination of the role played by short-channel effects (tox, LG, xj,Na). • Study of the role played by mobility by means of the IFM method.

  31. IUNET – MORE Contribution: Gate current simulations • IG-VG simulation through a multi-phonon trap-assisted tunneling model • Investigation of IG temperature dependencies: carrier-limited (depletion/ /weak inversion) and transport-limited (strong inversion) regimes • Identification of the atomic configuration of the defects assisting the electron (hole) conduction in nMOS (pMOS) devices nMOS - 1nm IL/3nm HfO2gatestack pMOS - 1nm IL/5nm HfO2gatestack

  32. Progress IUNET-Udine • Task 2.2.6(b) – reference NMX: Quantization • Extremely efficient Schroedinger poisson solver for rounded corner FinFET/wire structures • More than 100x speed improvement ExampleofSchr.-Poi.solutionforhexagonalwire [Paussaet al., SISPAD 2010, pp.234, accepted TED] Luca Selmi - IUNET-Udine - MODERN Progress report Nov. 2010

  33. Publications with MODERN ack. Luca Selmi - IUNET-Udine - MODERN Progress report Nov. 2010

  34. UNGL: D2.2.6 Contributions

  35. UNGL D2.2.6 Plans • Sensitivity analysis of Non-Volatile Memory performance as a function of individual trap position. • Couple sensitivity of trap position to other variability sources. • Outline of GSS approach to Toolbox methodologies.

  36. UNGL D2.2.6 Progress Work in progress to look at effects of single charge trapping and sensitivity of other sources of variability to charge trapping. NMOS PMOS NBTI/PBTI capabilities developed and used in D2.4.3 Initial studies of charge trapping carried out in D2.2.3

  37. UNGL D2.2.6 Toolbox GSS GARAND GSS Mystic GSS RandomSpice

  38. STF2 Contibution to D.2.2.6 • Using analytical MASTAR model, goal is to give a first outlook on device structure impact on variability at the 16nm node. Bulk, FinFET and FDSOI will be studied interms of SNM variation and Vdd,min variation. • Test case will be a 16nm 6T-SRAM Cell • Variability will be implemented on the following device parameters : Doping, Lgate, Electrode workfunction, film thickness variation (for FD devices), and mobility • Typical 3sigma variation inputs will be based on result obtained in MODERN of 45nm/28nm technology

  39. STF2 Contibution to D.2.2.6 • Example of results (20nm node)

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