Task 2 2 update
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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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Task 2 2 update

Task 2.2 Update

  • 5th October 2011

  • Agrate, Milano


Contents

Contents

  • Deliverables

  • Completed Deliverables

  • Final Deliverable


Contents1

Contents

  • Deliverables

  • Completed Deliverables

  • Final Deliverable


T2 2 deliverables

T2.2 Deliverables


T2 2 deliverables1

T2.2 Deliverables


Contents2

Contents

  • Deliverables

  • Completed Deliverables

  • Final Deliverable


Previously reported

Previously Reported


T2 2 deliverables2

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


D2 2 4 statistical variability in 32nm bulk cmos technology

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

UNGL Contribution


Task 2 2 update

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

IUNET Contribution


D2 2 4 statistical variability in 32nm bulk cmos technology1

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

RVT-NMOS

RVT-PMOS

RVT-NMOS

SNPS Contribution

RVT-PMOS


D2 2 4 statistical variability in nanowire technology

D2.2.4: Statistical Variability in Nanowire technology

IMEP Contribution


D2 2 4 statistical variability in dpd sic gan algan technologies

PCM STUDIO

EHD5 SEMICELL

SENTAURUS WORKBENCH

DOE

PCM

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

ST-I Contribution


D2 2 4 statistical variability in dpd sic gan algan technologies1

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

POLI Contribution


D2 2 4 compact modelling strategies for statistical variability

D2.2.4: Compact Modelling Strategies for Statistical Variability

UNGL Contribution

RVT-PMOS

RVT-NMOS


D2 2 4 drain current variability in 45nm bulk n mosfet

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

IMEP Contribution


T2 2 deliverables3

T2.2 Deliverables

  • What was accomplished?

    • UNGL: Creation and Study of Variability in 22nm FinFET

    • IUNET Contribution


D2 2 5 ungl variability in 22nm finfet

D2.2.5: UNGL: Variability in 22nm FinFET

UNGL Contribution


D2 2 5 iunet contribution

D2.2.5: IUNET Contribution

Deliverable delayed

but has been completed and submitted.


Contents3

Contents

  • Deliverables & Timeline

  • Completed Deliverables

  • Final Deliverable


Final t2 2 deliverable

Final T2.2 Deliverable

Plans and Initial Progress


Nmx contribution in collab with iunet mi task 2 2 d2 2 6

NMX contribution (in collab. with IUNET-MI)Task 2.2D2.2.6

Andrea Ghetti, Augusto Benvenuti


Investigation of rdf and rtn depedence on substrate doping

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


T2 2 publication list

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


Snps contribution t2 2 d2 2 6

SNPS ContributionT2.2D2.2.6


Implementations

Implementations


Plan for deliverable d2 2 6

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.


Iunet contribution for d2 2 6

IUNET contribution for D2.2.6

Alessandro Spinelli – UMET-MI in collab. with NMX

Susanna Reggiani – UNET-BO

Paolo Pavan, Luca Larcher – UNET-MORE


Study of rdf and rtn dependence on device geometry

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


Task 2 2 update

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.


Iunet more contribution gate current simulations

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


Progress iunet udine

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


Publications with modern ack

Publications with MODERN ack.

Luca Selmi - IUNET-Udine - MODERN Progress report Nov. 2010


Ungl d2 2 6 contributions

UNGL: D2.2.6 Contributions


Ungl d2 2 6 plans

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.


Ungl d2 2 6 progress

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


Ungl d2 2 6 toolbox

UNGL D2.2.6 Toolbox

GSS GARAND

GSS Mystic

GSS RandomSpice


Stf2 contibution to d 2 2 6

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


Stf2 contibution to d 2 2 61

STF2 Contibution to D.2.2.6

  • Example of results (20nm node)


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