Platform DA

A Complete Learning-Based Semiconductor Parametric Testing and Device Modeling Ecosystem

- from Probing to Simulation -

Yanfeng Li, Miao Li, Jian Yao, Riko Radojcic

albert_li@platform-da.com riko_radojcic@platform-da.com

http://www.platform-da.com

Platform Design Automation, Inc.- The EDA Platform Company

Platform DA

A Complete Learning-Based Semiconductor Parametric Testing and Device Modeling Ecosystem, from Probing to Simulation

Abstract

Use of Artificial Intelligence methods in general, and the application of specific optimization techniques, neural networks, and learning algorithms to semiconductor parametric test and model generation is described. In Parametric Test arena the goal is to improve the quantity and quality of the data by accelerating testing speed and breaking conventional test hardware constraints. Revolutionary “behavior-aware” testing methods implemented at the instrument level, DUT level and production test level are outlined. Use of learning algorithms to automate model generation is also outlined. The paper presents key concepts and summarizes some specific results.

Platform DA

OutlineCompany Introduction

Artificial Intelligence Overview

AI and Semiconductor Technology

For Test and Measurement

For Model Extraction

Case Studies for “Behavior-Aware” Test Methods

At instrument level: reduce settling time

At DUT level: curve recovery technique

At production test levels: reduce test samples

Case Study for Automate Model Generation

Bi-direction Network (BDN)

Conclusion

Platform DA

Outline

Platform DA

~$300B Semiconductor Industry

Platform Design Automation (PDA): Where We Fit

Process Domain(foundries, OSATS, Equipment Vendors,

Material Suppliers…)

Prog

ress

ivel

y m

ore

into

Dev

ice

Phys

ics &

Mat

eria

l Sci

ence

, etc

,,

Design Domain(Design Houses, EDA,

IP vendors, etc…)

Progressively More into System

A

rchitecture & H

igh Abstraction

Our Space

PDKModelDataTest SolutionPDKModelDataTest SolutionPDKModel

EDA+IP

DataTest SolutionTest Chip

Model & Simulation

Bridging the space between Si technology and DesignIntersect: Parametric Measurements, SPICE models and PDK’s

PDA: Unique Integration of All the Critical Elements that connect Process Technology with Product Design

Platform DA

Company IntroductionPDA : “Big Fish in a Small Pond”

Founded in July 2012Former Accelicon (acquired by Agilent in 2012)Experienced team with a solid track recordfrom Cadence, PDF Solutions, Keysight, Qualcomm...

Offering: Measure-Design-Process IntegrationIntegrated Measurement, Modeling and Design Solutions1-Stop design infrastructure services

Core CompetenceDevice characterization, modeling and PDKArtificial Intelligence (AI) algorithms

Value Proposition: Efficiency & Time & ValueFastest Test = Most DATAFastest Production Parametric Tests = Reduce TTMAlgorithms to break hardware constraints = Most Capable

Beijing Office

Beijing Lab

Shanghai Office

TW Hsinchu Office

Platform DA

Company IntroductionPDA : “Big Fish in a Small Pond”

Founded in July 2012Former Accelicon (acquired by Agilent in 2012)Experienced team with a solid track recordfrom Cadence, PDF Solutions, Keysight, Qualcomm...

Offering: Measure-Design-Process IntegrationIntegrated Measurement, Modeling and Design Solutions1-Stop design infrastructure services

Core CompetenceDevice characterization, modeling and PDKArtificial Intelligence (AI) algorithms

Value Proposition: Efficiency & Time & ValueFastest Test = Most DATAFastest Production Parametric Tests = Reduce TTMAlgorithms to break hardware constraints = Most Capable

Beijing Office

Beijing Lab

Shanghai Office

TW Hsinchu Office

Platform DA

Product and Service Portfolios

Artificial Intelligence

Test & Characterization Device Modeling PDK

FS360 & FS380NC300 & NC300L

MeQLabFastLab PQLab

Characterization Services Modeling Service PDK Generation Service+

Cell Lib & Compiler Service

Serv

ices

Prod

ucts

SEK

Platform DA

OutlineCompany Introduction

Artificial Intelligence OverviewAI and Semiconductor Technology

For Test and Measurement

For Model Extraction

Case Studies for “Behavior-Aware” Test Methods

At instrument level: reduce settling time

At DUT level: curve recovery technique

At production test levels: reduce test samples

Case Study for Automate Model Generation

Bi-direction Network (BDN)

Conclusion

Platform DA

Artificial Intelligence : Overview (1)• A lot of buzz about Artificial Intelligence

o AI: any device that perceives its environment and takes actions that maximize its chance of success at some goal

o Machine Learning: algorithms that avoid following static program instructions and can learn by building a model from sample input data and make data-driven decisions

o Deep Learning: based on learning data representations, vs task-specific algorithms. Learning can be supervised, partially supervised or unsupervised

o Deep Neural Networks : (DNN) is an artificial neural network (ANN) with multiple hidden layers between the input and output layers and can model complex non-linear relationships

o Recurrent Neural Networks: (RNN) is a class of artificial neural network (ANN) where connections between units form a directed cycle. This allows it to exhibit dynamic temporal behavior.

• Lots of Data + Cheap Compute Power• Data Driven Training Sets (Supervised and Unsupervised)

• Processing Power to Derive Models

• Applications• Computer Vision & Voice Recognition

• Autonomous Driving

• Oh and, Spam Filter, Credit Card Protection…

Platform DA

Artificial Intelligence : Overview (1)• A lot of buzz about Artificial Intelligence

o AI: any device that perceives its environment and takes actions that maximize its chance of success at some goal

o Machine Learning: algorithms that avoid following static program instructions and can learn by building a model from sample input data and make data-driven decisions

o Deep Learning: based on learning data representations, vs task-specific algorithms. Learning can be supervised, partially supervised or unsupervised

o Deep Neural Networks : (DNN) is an artificial neural network (ANN) with multiple hidden layers between the input and output layers and can model complex non-linear relationships

o Recurrent Neural Networks: (RNN) is a class of artificial neural network (ANN) where connections between units form a directed cycle. This allows it to exhibit dynamic temporal behavior.

Been around since 1956

What is New and

Why Now

• Lots of Data + Cheap Compute Power• Data Driven Training Sets (Supervised and Unsupervised)

• Processing Power to Derive Models

• Applications• Computer Vision & Voice Recognition

• Autonomous Driving

• Oh and, Spam Filter, Credit Card Protection…

This is Our World

… same old...

This is New-ishAnd driving the

buzz

Platform DA

Artificial Intelligence : Overview (2)• Spectrum of Algorithm Complexity & Data Requirements

Algorithm Complexity/Difficulty

Dat

a B

ehav

ior Autonomous DrivingFacial Recognition

Voice Recognition

Chess GOSelf Driving Trains

Text Recognition

Platform DA

Artificial Intelligence : Overview (2)• Spectrum of Algorithm Complexity & Data Requirements

Algorithm Complexity/Difficulty

Dat

a B

ehav

ior Autonomous DrivingFacial Recognition

Voice Recognition

GOSelf Driving Trains

• Our World is On the Low End of the Spectrumo Mostly constrained and predictable behavioro Lots of history and physics and many data pointso And no one dies if error rate is >0.000001%

SemiconductorParametric Data

Platform DA

OutlineCompany Introduction

Artificial Intelligence Overview

AI and Semiconductor TechnologyFor Test and MeasurementFor Model Extraction

Case Studies for “Behavior-Aware” Test Methods

At instrument level: reduce settling time

At DUT level: curve recovery technique

At production test levels: reduce test samples

Case Study for Automate Model Generation

Bi-direction Network (BDN)

Conclusion

Platform DA

AI and Semiconductor Technology• Technology Trends

o Shrinking Margins & Growing Variability

o Proliferation in Technology Flavors

• What Does the Industry Needo More Data

For process control

For Design Targeting

o Less Test TimeConstrain Costs and Enhance Productivity

• Opportunity for Application of AIo Enhance Accuracy = Resolve Variability

o Accelerate Test = Reduce Test Cost

o More DFM = Models

Platform DA

AI and Semiconductor Technology• Technology Trends

o Shrinking Margins & Growing Variability

o Proliferation in Technology Flavors

• What Does the Industry Needo More Data

For process control

For Design Targeting

o Less Test TimeConstrain Costs and Enhance Productivity

• Opportunity for Application of AIo Enhance Accuracy = Resolve Variability

o Accelerate Test = Reduce Test Cost

o More DFM = Models

Motherhood & Apple Pie

But Still True

Platform DA

Standard flow of machine learning

Data Pre-processingFeatureextractn.

Featureselection

Inference,Prediction,Recognition

Slide Courtesy: Andrew Ng, Kai Yu

• Most critical for accuracy• Account for most of the computation for testing• Most time-consuming in development cycle• Often hand-crafted in practice

Most Effort in

Machine Learning

How do we apply this standard flow specifically for IC Industry?

Feature Representation Learning algorithm

0

2

4

6-25 -20 -15 -10 -5

0 5

-15

-10

-5

0

5

10

-Gain(dB)NF(dB)

-IIP

3(dB

m)

SamplesPareto SetPareto Front

Platform DA

Step 1: DataData Pre-processing

Featureextractn.

Featureselection

Inference,Prediction,Recognition

Traditional

Measure

Store

New Era

Measure More

Store &

Mine

• Measurement does not go Away (there is no magic)o In fact : need more of it => Must be faster & cheapero New effects, New variability, Less margin…

• Must Leverage the Data to Enhance Measurementso Data mining shaping the data gathering

Platform DA

Step 2: Feature Representation

Data Pre-processingFeatureextract.

Featureselection

Inference,Prediction,Recognition

Data

Data Simplification

• Computer vision preprocessing

Slide Courtesy: Andrew Ng

IC industry Data Compression Techniques

• Information Compressiono Principal Component Analysis (PCA):

successfully used for corner modelso Fast Fourier Transformation (FFT):

common in signal processing

• Standard Practices used in Semiconductor Technology are Analogous to Procedures Used for high end AIo Techniques such as PCA, FFT, Optimization…o Vs preprocessing for computer vison

Platform DA

Step 2: Feature Representation

Data Pre-processingFeatureextract.

Featureselection

Inference,Prediction,Recognition

Data

• Analogous Algorithms and Structures may be Appliedo Artificial Neural Network (ANN) to imitate braino Convolutional Neural Network (CNN)o Recurrent Neural Networks (RNN)

Deep Learning Methods

• Auto Feature Extraction

IC Industry Modeling Practices

• Optimizationo Unsupervised learning between

measurement and modeling to reach the optimized SPICE model

Platform DA

Step 3: Inference & Prediction

Data Pre-processingFeatureextract

Featureselection

Inference,Prediction,Recognition

AI Methodologies• Late 80’s

o Neural Networks；Boosting；Support Vector Machines；Maximum Entropy• Since 2000 – learning with structures

o Kernel Learning; Transfer Learning; Manifold Learning; Sparse Learningo Matrix Factorization; Structured Input-Output Prediction;

Faster MeasurementsAutomatic Modeling

Automatic Design ?

Mainstream Applications Semiconductor Applications

Platform DA

Step 3 : in Semiconductor Technology• Traditional Version of

“Prediction & Inference” => Modeling & Simulationo Use Fitting & Interpolation

Techniques to Optimize Model

o Leverage Physical Models to Define/Constrain Curve Shape

• AI Opportunity? : Relativity Learning

o define relativity function (relationship) between each point

o Analogous to techniques like in models used to predict motion tracking in graphic processing

• Opportunity to Use AI and Do Things Differently ?

Platform DA

Learning Technique vs. Traditional Fit

BJT

DiodeResistor

MOS

一阶导

Blue：Relativity LearningRed：Spline

Blue：Relativity LearningRed：Spline

Blue：Relativity LearningRed：Spline

Blue：Relativity LearningRed：Spline

Platform DA

OutlineCompany Introduction

Artificial Intelligence Overview

AI and Semiconductor Technology

For Test and Measurement

For Model Extraction

Case Studies for “Behavior-Aware” Test Methods At instrument level: reduce settling time

At DUT level: curve recovery

At production test levels: reduce test samples

Case Study for Automate Model Generation

Bi-direction Network (BDN)

Conclusion

Platform DA

Case Studies: At instrument level

• Reduce Settling Timeo AI for Adaptive Starting Point Selection

• Result : accelerated test time

Traditional Starting Point

Real Signal

Super Fast Starting Point

Time Series Analysis (TSA) prediction

Fast Starting Point

Machine Learning (ML) prediction

Platform DA

Case Studies: At DUT level

Historical and Relative datao e.g. same device / different bias point

o e.g. same TEG / different device size

o e.g. same wafer / different TEG

o e.g. same structure / different wafer

o etc…

Verified model o e.g. BSIM I—V, 1/f flicker noise..

Curve Recovery algorithmso e.g. Relativity Learning

• Principle: Leverage Expected DUT Behavior to Improve Testo DUT Characteristics are Known and can be Anticipated

e.g. I-V, C-V, 1/f… vs L,W,T…

Platform DA

Case Studies: At DUT level• Many Opportunities to Accelerate Test w/o Loss of Accuracy

• e.g. Pre-Set the SMU Range o since you know what to expect

• e.g. ‘Sparsify’ Test Step Sizeo Sample & Curve Recovery o Relativity Learning algorithms

Can still extract all desired derivatives

• e.g. De-embed signal from noiseo By applying suitable domain transformationso e.g. device noise vs test system noise

simpl

e =>

com

plex

Platform DA

Case Studies: At Production Test level

• Bad Case Detectiono e.g. Bad Probe Contacto e.g. Bad Device, TEG or Wafer

1. Use Machine Learning to Define Envelope of Expected Behavior

Inc. all SPC allowed Variability

2. Compare Measured Data to Expected Values

3. If Data Outside Envelope => Abort

• Simple – But Effective (Do not Collect Bad Data that is Dumped Later)o Esp. Valuable for Long Automated Test Routines

e.g. Overnight WAT Test

Platform DA

OutlineCompany Introduction

Artificial Intelligence Overview

AI and Semiconductor Technology

For Test and Measurement

For Model Extraction

Case Studies for “Behavior-Aware” Test Methods

At instrument level: reduce settling time

At DUT level: curve recovery technique

At production test levels: reduce test samples

Case Study for Automate Model Generation Bi-direction Network (BDN)

Conclusion

Platform DA

Model Generation Practices

• State of the Arto BSIM Model: Fundamental principles

haven’t changed in 20 years.

o Fitting targets (Ion, Vth, Gm, etc.) haven’t changed for 20 years

Have a lot of expertise & data and we accept some fitting errors

ParameterTweaking

ChangingCriteria

• Traditional Approacheso Curve fitting (e.g., linear regression)

Require physical inputPre-defined curve shape

o Curve interpolation (e.g., spline)Noise in dataRunge's phenomenaCannot derive Confidence Level

Platform DA

Model Generation Practices

• State of the Arto BSIM Model: Fundamental principles

haven’t changed in 20 years.

o Fitting targets (Ion, Vth, Gm, etc.) haven’t changed for 20 years

Have a lot of expertise & data and we accept some fitting errors

• Ideal Application for Bidirectional Recurrent Neural Networks (BRNN)

• do not require input data to be fixed.

• future input information is reachable from the current state.

ParameterTweaking

ChangingCriteria

Platform DA

Model Generation vs Model Selection

• Which is Best ?

• Depends on Design

• The real ‘art’ is in understanding the tradeoffs and selecting the right solution for a given application – not in tweaking parametersor

Platform DA

Case Study：BRNN for Modeling• Apply BRNN to Produce a set of Proposed Models

1. Use Machine Learning to ‘propose’ various BSIM Parameters 2. Device Engineer Selects the Best Match for his Target3. Supervised training per node and per device type => improve over time

• Produce multiple “answers” based on Machine Learning o No iterative optimization and parameter tuningo Proposed Candidates do Not have to be exact (e.g. SIRI vs Search Engine)

• Modeling engineers ‘Train’ the system and Select the Best Optiono “Judges” rather than spending time on tweaking parameters

• AI replaces labor not expertise o e.g. know your design needs & use expertise to select the best solution

3 Minutes/Round3 Minutes/Round40x23=920 specs40x17=680 parametersQA constraints

Platform DA

OutlineCompany Introduction

Artificial Intelligence Overview

AI and Semiconductor Technology

For Test and Measurement

For Model Extraction

Case Studies for “Behavior-Aware” Test Methods

At instrument level: reduce settling time

At DUT level: curve recovery technique

At production test levels: reduce test samples

Case Study for Automate Model Generation

Bi-direction Network (BDN)

Conclusion

Platform DA

Practicing DARK Arts

We are optimization experts with daily exercises with different neural networksAlgorithms

Accumulated millions of curves for different foundry/processesData

Knowledge Modeling background gives us the best knowledge in device/IC behaviors

Risk We Should expect and Manage the error in simulation or measurement

• AI Looks Very Promising => We should Embrace Ito Not Mysterious Magic

• Semiconductor Technology is Characterized by Contained Scope o This is Important & Makes Application of AI Easier

• Leave out the Fancy Words and Lets Get Practical with AI !