a complete learning-based semiconductor parametric ......platform da a complete learning-based...
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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
[email protected] [email protected]
http://www.platform-da.com
Platform Design Automation, Inc.- The EDA Platform Company
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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.
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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
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Platform DA
Outline
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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
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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
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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
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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
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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
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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…
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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 ?
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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
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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
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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
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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…
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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
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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
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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
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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
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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
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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
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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
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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
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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 !