Cloud-Accelerated Innovation for Semiconductor Design and … · 2019-11-22 · AWS custom...

David Pellerin

Cloud-Accelerated Innovation for Semiconductor Design and Verification

Head of Worldwide Business Development, Semiconductor IndustryAmazon Web Services

Amazon is in the semiconductor business

We design our own silicon devices, and we source from a global supply chain

Amazon has multiple, globally distributed silicon teams, for • Datacenter infrastructure• Consumer devices• Robotics and AI• And moreWe benefit from cloud in our own silicon development processes

AWS global Cloud infrastructure

22 geographic regionsA region is a physical location in the world where we have multiple Availability Zones

69 Availability Zones Distinct locations that are engineered to be insulated from failures in other Availability Zones

NetworkAWS offers highly reliable, low latency, and high throughput network connectivity. This is achieved with a fully redundant 100 Gbps network that circles the globe .

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

AWS custom hardwareThe AWS Global Infrastructure is built on Amazon’s own custom hardware By using our own custom hardware, we provide customers with the highest levels of reliability, the fastest pace of innovation, all at the lowest possible cost AWS optimizes this hardware for only one set of requirements: workloads run by AWS customers


AWS completes multiple silicon tape-outs each year

Powerful and Efficient for Server for

Modern Applications

Cloud Hypervisor, Network, Storage,

and Security

Machine Learning Inference Hardware

and Software at Scale



Silicon design workflow

Design verification Synthesis Physical

layoutPhysical verification

Tape out/manufacturing

Power/signal analysis

Design specification

Silicon validation

For advanced node design, the pain is here








Silicon validation

Requiring ever-larger computing for signoffCloud is becoming the new signoff platform


Design and verification on CloudOn the Cloud, secure and well-optimized compute and storage chambers can be created, operated, and torn down in just minutes

Machine learning and analytics cloud services

Third-party IP providers and collaborators

Virtual Private Cloud on AWS

Remote desktop

License managersWorkload schedulersDirectory services

Cloud-based, auto-scaling compute clusters

Shared file storage Storage cache

Corporate datacenter

On-premises HPC resources

AWS Direct Connect

AWS Snowball

EDA, CAE, analytics, and machine learning

TSMC Virtual Design Environment (VDE)

Validated by TSMC for…• Security of IP, workflows, and tools• Performance: compute , memory, networks, storage• Automation and cluster/job/license management• Remote graphics for interactive applications• CAE/EDA and IP vendor support

Developed in collaboration with TSMC,Synopsys, Cadence

Virtual Private Cloud on AWS

Remote desktop

License managersWorkload schedulersDirectory services

Cloud-based, auto-scaling compute clusters

Shared file storage Storage cache

Astera Labs Develops Complex SoC 100% on AWS

Industry: Semiconductor and Electronics

Headquarters: San Jose CA

Website: www.asteralabs.com

At Astera Labs, we are intensely focused on delivering high-quality PCIe connect ivity solut ions to our customers, and reduce t ime-to-results.

Our High-Performance Compute (HPC) infrast ructure is hosted ent irely on AWS and we heavily leverage the cloud-scalability enabled by AWS and Synopsys tools to accelerate our development schedule.

Jitendra Mohan, CEO Astera Labs

“

”

About Astera Labs

Our vision is to be the t rusted partners to dist ribute data in intelligent systems. We develop purpose-built connect ivity solut ions to remove performance bot t lenecks in data-centric systems and work-load opt imized plat forms. The company’s product port folio includes system-aware semiconductor integrated circuits, boards and services to enable robust PCIe connect ivity.


MediaTek7nm tapeout

• Proven results for EDA running on Cloud• Static Timing Analysis (STA) for 7nm process SoC• 1000 AWS instances (32,000 physical cores)• 12 million core -hours of computing for STA

• Successfully eliminated IT compute resource bottleneck• World’s 1st 5G SoCannounced at Computex 2019 (May 29th)

Samsung 7LPP Synopsys reference flow on AWS

ARM Cortex A53_CPUFrequency 2.16 GHz Physical area 232427 (um2)

Performance on AWS (32 CPU, 480G RAM)Full Reference Flow Runtime 71 HoursPeak Memory 24 GB

Cadence reference flows for Samsung Foundry nodesIncluding 28FDS, 14/11, 10/8, 7/5/4 EUV

• Full Cadence Digital Reference flow synthesis-through -DFM on Samsung Foundry nodes• Proven and validated for customer use on the cloud








Silicon validation

Advanced node design requires better use of dataincluding analytics and machine learning

Design for manufacturability

Materials and processes, yields and failure rates


Technology partners for silicon design: examples


layoutPhysical Verification




Silicon validation


Autonomous Robots

Simulat ion and Digital Twin

Vert ical & Horizontal Integrat ion

Indust rial Internet of Things

CybersecurityCloud

Addit iveManufacturing

AugmentedReality

Big Data & Analyt ics

Cloud is one of the 9 standalone enablers

Cloud is the cent ral innovat ion enabler across all disciplines and indust ries

Industry 4.0 – technical enablers


Cloud enables secure partner collaboration

EDA/IPcollaboration cloud chamber

Yieldanalytics cloudchamber

FoundryIP merge cloud chamber

Digitaltwin cloudchambers

EDA vendorsecure cloud chamber

IP provider secure cloud chamber

Fablesssemico secure cloud chamber

Fabless semiconductor

Equipmentprovider secure cloud chambers

Foundry/OSATsecure cloud chamber

Foundry/OSAT

PDK

GDSII

Tools

IP

Curated OT and machine data

Updated ML models

Col

labo

ratio

nC

ham

bers

Sing

le T

enan

t Cha

mbe

rs


Machine Learning and IoTfor SemiconductorsApplications throughout design and production

Design and verification …• Intelligent local and

global routing• Timing analysis and DRC• Simulation parameter selection• Design flow optimization• And more

Manufacturing and supply chain …• Lithography optimization• Fault detection and classification• Yield diagnostics and failure prediction• Predictive maintenance and OEE• Excursion prevention• And more

Engineering & operational DATA

Ingest ConsumeStore Analyze

Engineering & operational INSIGHTS

Wafer defect classificationCustomer seeks to reduce the need for human inspection to classify wafer defects (scratches, spots, bubbles)SolutionUse SageMakermachine learning on AWS Cloud, in combination with API Gateway, DynamoDBdatabase, AWS Lambda, and cloud storage to create an environment for model training and image classification..

Machine learning in the fab


Cloud Manufacturing Reference Architecture: Edge-Cloud-Edge

IoT GreengrassEdge/GW

S3Manufacturing

Data Lake

Kinesis

MES

Factory Equipment

ML Inference

IoT Core

SageMakerML

QuickSightBusiness

Intelligence

Athena

Data Historian

Storage Gateway

EMR

EBS EC2 Batch AppStreamEBS EC2

E&D Workloads(PLM/EDA/CAE)

Enterprise Workloads(SAP ERP/CRM)

DMS RDS

Local Servers

RedshiftData Warehouse

Dat

a In

gest

ion

API

IoT SiteWise

Snowball Edge

Connected Products

DynamoDB Lambda

IoT Core

Amazon Forecast

IoT GreengrassConnectors

IoT Analyt icsAnalytics

Timest ream

Outposts

IoT Events

EC2

LambdaBusiness Logic

SNS

Connected Vehicles

Foundry/OSAT AWS Cloud

Smart Product

Manufacturing Applications

OPC-UA

Modbus

Customer Connector

EDGE CLOUD EDGE

Partnership with OPTIMAL+Cloud

Large data sets, powerful analytics

• Data aggregation and storage• Analytics tools and massive computing• AI/ML modeling and simulation tools• Edge deployment tools• IoT device management and security

EdgeReal-t ime, low latency, inferencing

• Real-t ime data collect ion/ harmonizat ion• Real-t ime analyt ics• Real-t ime AI/ ML inferencing• Real-t ime data feed-forward/ feed-backward• Machine cont rol and adapt ive manufacturing

Cloud infrast ructure – System architecture expert ise – Edge infrast ructure – Domain expert ise

AssemblyData

OPTIMAL+ and AWS: Edge-Cloud-Edge

Data Collection

DataTransformation

DataCleansing

O+ Data Acquisition

O+ analytics engine

O+ Edge Analytics

O+ Sequoia analytics designer

O+ Central

Portal+Rules+

O+ DataLoading

SageMaker

IoT Core S3

Lambda

GreenGrass

1. Select Training Population

2. Pull Data

3. Build Model

4. BuildInferenceService

5. Deploy to Greengrass

7. Publish Rule (Actions Instructions)

6. Define Rule

9. Inference results

10. A

ctio

n 0. Data Highway

8. Micro -batchData

Factory(internal or outsourced)

O+ data platform

Tester X Other equipment

& data sourcesO+ Proxy

Tester A

O+ Proxy

FabData

MES

Machine Learning use case – example*Use Case Skip/optimize test operation

Skip/optimize downstream operation (e.g. SLT/BI) leveraging upstream operation data (e.g. WS and FT)

Customer Value Significant OPEX and CAPEX savings and increased throughput

Why Machine Learning?1. High dimensional data2. Multi -variant data

relations3. Non-linear data

relations

* Example – this machine learning use case is just one example for many use cases that can be addressed by using the O+ and AWS integrated solution

Test operation

WS

Out

PassTest

operation FT

Out

PassPredicted

failTest

operation BI

Out

MLModel

Skip BI

Pass

Sampling for validation

Example: Burn-in• ML model shows

opportunity of reducing Burn-in by 20% with 0 impact to DPPM

• This allows to define the threshold for the manufacturing Reliability -IndexRI threshold

• Drilldown to most important features

• In this use case, the top 30 predict ive features cover ~ 95% of overall predict ive power of the ML model

© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

Unlocking relevant, actionable information buried in wafer fabrication, process control and test data

Enabling secure collaboration without compromising sensitive data

PDFS Exensio® on Cloud

Customer 1SecureCloud

Environment

Customer 2SecureCloud

Environment

PDFSSecureCloud

Environment

PDF

Customer-1 HQ

Customer-2HQ

PDF Solutions and AWS

© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

Example: Early Life Failure Detection (ELF)Machine Learning to predict likelihood of Early Life Failure in the field

PCM/WAT

Final Test

Sort

AssyFDC

BurnIn

Signature Library

Die/ PkgReliability Grade

A Grade

Down Grade

Manual Review

“Expert”Collaborative Learning

Defectivity

Metrology

Spatial Outlier

Ensemble

SYLSBLSTLSPL

Test OutlierEnsemble

EquipmentVariance

Process Exceptions

Reliability Indicators

ML

Scrap

Multiple data types, multiple algorithms, machine earning, potentially large data sets, collaborative learning …


Semiconductor innovation enabled by Cloud

Flexible configuration and virtually unlimited scalability to grow and shrink your EDA infrastructure

Secure collaboration and supply chain analytics for semiconductor development and manufacturing


Thank youITPC 2019

David PellerinHead of Worldwide Business Development, Semiconductor IndustryAmazon Web Services

[email protected]

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Cloud-Accelerated Innovation for Semiconductor Design and … · 2019-11-22 · AWS custom...

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