Meeting the Design Challenges of Nano-CMOS Electronics Asen Asenov (PI), Richard Sinnott (eSDirector), all partners e-Science Pilot Project 11 PDRAs 7 Science 4 e-Sci 9 PhD £3.7M EPSRC £4.5M FEC £5.7M with IC

I have been here before NASA Late 90ies Information Power Grid IPG Glasgow was the overseas node

Content  The partners  Motivation  The challenge  The eScience and Grid approach  Current status  The next phase

Advanced Processor Technologies Group (APTGUM) Device Modelling Group (DMGUG) Electronic Systems Design Group (ESDGUS) Intelligent Systems Group (ISGUY) National e-Science Centre (NeSCG) Microsystems Technology Group (MSTGUG) Mixed-Mode Design Group in IMNS (MMDGUE) National e-Science Centre (NeSCE) e-Science NorthWest Centre (eSNW) University Partners

Industrial Partners Global EDS vendor and world TCAD leader 600 licences for grid implementation, model implementation UK fabless design company and world microprocessor leader Core IP, simulation tools, staff time UK fabless design company and world mixed mode leader Additional PhD studentship for mixed mode design Global semiconductor player with strong UK presence Access to technology, device data, processing Global semiconductor player with strong UK presence Access to technology, device data, processing Global semiconductor player with UK presence CASE studentship, interconnects Trade association of the microelectronics industry in the UK Recruiting new industrial partners and dissemination

The Challenge Toshiba 04 Device diversification 90nm: HP, LOP, LSTP 45nm: UTB SOI 32nm: Double gate

The variability is becoming a major headache G. Declerck, Keynote talk, VLSI Technol. Symp. 2005

Deterministic variability

Statistical variability The simulation Paradigm now A 22 nm MOSFET In production 2008 A 4.2 nm MOSFET In production 2023

V out 1 [V] V out 2 [V] Statistical variability

Performance/Power/Yield (PPY) trade-off 90nm TN 45nm TN

Delivering new results Simple concept Integrated Hierarchical Statistical Design Complex data and workflows Data and Compute Intensive Security Sensitive

Objectives  Understanding and forecasting the behaviour, characteristics and variability of next generation nano-CMOS devices using Grid-enabled statistical 3D numerical simulations;  Developing compact model and parameter extraction strategies to capture new device characteristics and variability (based on physical device simulation and early research devices), creating efficient databases for circuit design;  Developing hybrid, Grid based device/circuit simulation techniques applicable to novel nano-CMOS devices with significant variability;  Investigating the impact of new device architectures and device variability on well established design components, sub-systems and systems presently designed using conventional MOSFET architectures;  Developing novel design concepts that cope with increased variability, using specific properties of the new devices;  Learning how electronics researchers can use e-Science technologies to support their work, improve their productivity and enable them to undertake research hitherto impossible.

The Development Challenge Electronic design teams currently use different tools, have different data formats, need access to large scale compute resources, generate vast amounts of data, work independently of device engineers. We are building an integrated Grid infrastructure which will revolutionise nanoCMOS design by hiding the complexity of the statistical design making it a completely integrated collaborative process.

Dealing with the complexity Year 1 DMGUG NeSCG NeSCE eSNW Year 2-4 ALL

Synopsis.tif file input Manual Extraction Of Doping Profiles Doping Profiles Current Workflow

Doping Profiles Current Workflow Manually generated Input file + + Atomistic Simulation Simulation Time Of between 1K-50K CPU Hours

Current Workflow Atomistic Simulation x High Drain Bias IV’s x Low Drain Bias IV’s Mb Data Synopsys Aurora Shell Scripts Simulation Time Of between 1K-50K CPU Hours

Current Workflow Synopsys Aurora Aurora Extraction Strategy Uniform Device Simulation Data Synopsys Aurora Aurora Extraction Strategy x Data files Spice Compact Models

Grid stretch focused on realising scientific needs Optimised nanoCMOS Grid Infrastructure Resource Mgt Framework Data Mgt Framework Advanced Security Framework Workflow Mgt Framework Security attributes definition Policy Decision/Enforcement Points Attribute Request/Release Policies Identity Management Trust FederationAccounting Components Information Services Meta-scheduling Services Reservation/Allocation Services Resource Broking Service Data Access/Linkage/Integration Replication/Movement Metadata/Provenance Storage/Curation Services Domain Knowledge Capture/Pres. Data Transformation Workflow Definition Robust Enactment Dependency Mgt Job submission/mgt Computational Steering Visualisation Services

Balsa high-level asynch. circuit synthesis tool used, e.g. for timing verification Expressivity of myGrid Taverna workflow design, FreeFluo enactment? my Grid workflow => Grid service development Meta-data capture/data annotation Control loops for optimisation, concurrency needed Feed requirements into OMII-UK for language and enactment engine enhancements Typical existing application

 Can solve licensing issues on Grid  451 group identified this as key area Grid community must address (…IECnet)  Fine grained authorisation readily supported by Shibboleth and associated technologies such as PERMIS  Is being deployed across UK academia to replace existing Athens system  essential to address gap between research and Grid communities  consider number of active UK e-Science certs vs Athens accounts  Future Grids must be harmonised with wider e-Infrastructure developments  Using the Grid should be no different than using any other internet based system from researcher perspective Why Shibboleth…?

DyVOSE Delegation Issuing Service PERMIS based Authorisation checks/decisions Glasgow Education VO policies GlasgowEdinburgh Condor pool Grid BLAST Data Service Nucleotide + Protein Sequence DB Grid-data Client Grid BLAST Service Edinburgh Education VO policies LDAP Implemented by Students Protein/nucleotide sequence data returned based on student team and Edinburgh policy data input Job scheduling/ data management Glasgow SoA using Edinburgh DIS Glasgow nanoCMOS policies Edinburgh nanoCMOS policies Edinburgh nanoCMOS data sets Edinburgh nanoCMOS services Glasgow nanoCMOS researchers Glasgow nanoCMOS services Create new ACs for Glasgow nanoCMOS users/roles

Roles, attributes, licenses,… needed to make authorisation decision Distinguished Name Shibboleth-based access to/usage of Grid Resources

Shibboleth Enabled Portal - The New Workflow Institutional Login

The New Workflow - Unified Simulation Logged In To Portal Shibboleth Attributes determine permissions from attributes

The New Workflow - Unified Simulation Stage 1: Create a New Job Ticket This will be automated in the very near future

The New Workflow - Unified Simulation Stage 2: File Upload The desired.tif doping profile and atomistic input file are uploaded to the server

The New Workflow - Unified Simulation Stage 3: Atomistic Simulation Extraction, mesh generation, simulation, and aurora output phases are now combined

The New Workflow - Unified Simulation Stage 4: Aurora Simulation Input the extraction strategy that you wish to use and run aurora.

The New Workflow - Unified Simulation Stage 5: Your compact models sir Select the data you require and download it.

 OMII-Security Portlets to developing family of JSR-168 compliant portlets for:  scoping of attributes, e.g. only accept Shibboleth attributes and assertions from nanoCMOS partners  dynamic portal configuration management, e.g. configure portal content based on user privileges (security attributes)  attribute release policies, e.g. only release my nanoCMOS specific attributes to nanoCMOS partners  OMII-RAVE  With Cardiff University to integrate Resource Aware Visualisation Environment into OMII  Visualisation key component and will help computational steering Other new projects supporting nanoCMOS

MSTGUG Cell fragments Reduce deterministic variability: develop regular cell fragments optimise the fragments to reduce deterministic variability Characterise the statistical variability full 3D simulation of statistical variability in the fragments timing variability

APTGUM Balsa synthesis World’s leading public domain async synthesis tool developed at UoM multiple back-end libraries with differing timing assumptions Results from eScience: improved understanding of economy/reliability trade-offs on future technologies

ISGUY Redundancy for fault tolerant design Different hardware Configurations can meet the fitness criteria

ESDGUS World-leading research Behavioural Simulation (VHDL-AMS) Fault Simulation/Modelling Results from eScience: Grid-enabled simulation technology Behavioural Analogue Fault Simulation