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Cognitive End to End Analytics for Semiconductor Manufacturing: A Smart Testing Application

October 30, 2019 Kenneth Harris, Ph.D.

This presentation contains forward-looking statementsregarding PDF Solutions’ future products and businessprospects that involve risk and uncertainty. Actual resultscould differ materially from those discussed. You shouldreview PDF Solutions’ SEC filings, including its annualreport on Form 10-K and quarterly reports on Form 10-Q,for more information on these risks and uncertainties. PDFSolutions does not undertake an obligation to update anysuch statements.

© 2019 PDF Solutions, Inc. All rights reserved.

PDF Solutions: Best-in-Class Big Data Analytics for Semiconductors

4

>160Patents Issued

> 350 Employees

>100 Ph.D.

>200 Advanced Technical Degree

Market Cap

$522M

1991Founded

PDFSNasdaq

Exchange / Ticker

HQ Santa Clara, CAUSA

and 200+ patents pending

PDF Solutions, All Rights Reserved

Industrial Partners and Supported Platforms

PDF Solutions, All Rights Reserved 5

> 50 vendors and

> 150 equipment models for assembly

>40 vendors and

> 100 equipment models for

manufacturing

> 20 vendors and

> 50 tester / prober / handler models for

test Direct tool connections are used by customersIndustry spends $60B in capital investment annually

TIBCO PDF Partnership

PDF Solutions:– Leader in semiconductor services and analytics

infrastructure.

– Providing Yield Ramp services to global customers for over 20 years down to 7nm node.

– Over 4PB customer data managed, 24K process tools under control.


TIBCO:– Integrated analytics solutions ranging from data

exploration to embedded business intelligence

– Open source architecture to enable customization to specific applications

+

1 + 1 > 2

The aim of Industry 4.0 and Made in China 2025 is to improve mfg by digitizing the manufacturing process

Key elements being put in place on the path to digitizing the IC manufacturing process

– Sensors – Unique data capturing all essential behavior

– Integration – common platforms to provide access to data from the entire value chain

– Predictive Analytics – AI/ML tools able to be leveraged in manufacturing against…

– …Semantic models (in digital twin applications) that allow meaningful and relevant insights to be drawn and actions pushed back to the execution systems

– Direct connection to the tools: Can’t ACT if you don’t change tool state

– Site-to-site connectivity: Must integrate data across the entire supply chain

Industry 4.0 Being Applied in Many Manufacturing Markets

Source: Deloitte University Press.https://www2.deloitte.com/insights/us/en/focus/industry-4-0/digital-twin-technology-smart-factory.html#figure-1

Industry 4.0 Cycle


Today’s Struggle: Silos and Local Optimization


Execute

InspectAnalyze Data

Adjust

Process / Test 1

100TB unused data!

• One step learning• One step adjustment• Data used once• Human heavy

Higher costLower qualityFalling yields

Process / Test NProcess / Test 3Process / Test 2

Customers want to be able to do something better than humans stuck in silos

By integrating data and applying ML, better results can be achieved.

Execute

InspectAnalyze Data

Adjust Execute

InspectAnalyze Data

Adjust Execute

InspectAnalyze Data

Adjust

A Unified View of Semiconductor Data is Needed

WaferTestData

Semi Mfg Wafer Sort Final Test System-Level TestAssembly

Materials DataProcess DataMetrology Data

Performance DataReliability DataWire-Bond Data

In-Field Monitors

TrendingField Returns

Wafer-level grading and disposition

Test reduction and adaptation

Die quality and RMA prediction

Yield prediction

Fault detection and classification

Capacity and efficiency improvement

Predictive maintenance

Virtual metrology


Semantic Models: A Key Element for Applying Machine Learning

The semantic data model is a method of structuring data in order to represent it in a specific logical way.

Examples:

– Aligning events in a fab with wafer data to answer question like “which wafers were processed with the new batch of resist”?

– Meaningful merging of chip data as the chips flow through wafer sort, assembly, and final test


Semantic models enable deployment of machine learning to production

Multi-chip Module (MCM) assembly is complex

11

Todays packages contain many active and passive components

Any of these represent a reliability and security risk

Components both and without ECID can be tracked


Handling any material flow (some examples)

SEMI E142 defines a semantic model for substrates used in manufacturing.

MCM packages may be assembled on a strip from

– wafer, waffle pack, tape and reel, etc.

12

Strip to Tray to Tape and Reel to PCB

Tape & Reel(s) to Strip

Wafer to Tape & Reel


Tracking all MCM components and consumables

Die Attach

– 3 different die assembled into a 5 die MCM

Wire bond

– Gold wire lot is recorded

Laser Mark

– Strip map uploadedwith Device ID

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Wire Bond

Laser Mark

Epoxy

Gold Wire

Device 1 Attach

Device 2 Attach

Device 3 Attach

Unique 2D ID marked on Package

Bin code records pass for wire bond

Bin code records fail (unreadable) for laser mark

Bin code records fail for device attach


Exensio provides map visualization based on SEMI E142 Sematic model (based on SEMI

Standard E142)

Single click browsing to trace where each device came from (e.g. Wafer ID + XY)

Root cause analysis to assembly process, step, equipment, etc.

14

Customized visualizations, unique data sources, sematic models, and Spotfire-interactivity is critical ensuring quality parts.


Identification of failure pattern on wafer from final test fails

15PDF Solutions, All Rights Reserved

Disaggregated Supply Chain

Increasingly sophisticated functions of design, fab, assembly, test has led to specialization in the value chain.

N x N x N problem (Fabless x Foundry x OSAT)

PDF Solutions, All Rights Reserved16

OSAT

OSAT

OSAT

OSATFabless ICCompany

Fabless ICCompany

Fabless ICCompany

Foundry

Foundry

Deployment Challenges


Foundry

OSAT

OSAT

Data

Inference engine

Prediction

Data lossSecurity riskTime delay

Training engine

OSAT

DEX™ Enables Edge Deployment Architecture


Foundry

OSAT

OSAT

Data

Inference engine

Model

Training engine

“THE EDGE”

OSAT

Edge Analytics – fast turnaround times on predictions, making ACTIONABLE predictions a reality

Reduced data loss, improved data quality

Allows user to develop their own models using latest ML technologies

Deployed at major OSAT subcontractors and customers -- solves N x N x N problem.

Real-Time Test Floor Monitor – Tester Detail

Provides real time visibility into real time tester events.

Real-time tester data captures status from Cell Controllers

Data Exchange (DEX) responsible for transport and storage.


Model training and prediction pipeline


Filter & feature

engineering

AUC feature selection + imputation

GroupingMultivariate

feature selection

Relabel targets to

reduce class imbalance

Ensemble / Parameter

selection for Final model

Filter & feature

engineeringImputation Grouping Predictions

Update table &

output to folder

Data Preparation & Feature generation Feature Selection Model Training / Execution

Training

Prediction

• Handle incomplete data (retests etc)• Clip extreme values & impute missing data• Remove highly correlated features• Adjust to shifting input data schema

• Tree-based classifiers• Proximity based classifiers• Linear classifiers

Smart Testing

Methodology

Some challenges for ML in the Semiconductor Industry

Multimodal batch trajectories due to product mix

Test program changes

Process drift and shift, tool recalibration

Changing failure modes

Small amount of training data

Lack of labels

Lack of data for emerging technologies

Lack of traceability for root cause


What happens when your data doesn’t arrive? What do you do if it is corrupt? What kind of prediction do you make?

https://www.semanticscholar.org/paper/Principal-component-based-k-nearest-neighbor-rule-He-Wang/464e2caec9ce4b638df7fb557064b9e3bd46d51d/figure/4

Smart Testing


Inline Fab

DataWAT

Test Station1

Test Station2

Decision Node

ASSYFinal

Test 1Final

Test 2

Input Data Output Data

Target

Goal: No risky chips to field

Goal: Improve quality and reliability

Goal: Focus test resources on at-risk products

Goal: Reduce test cost

Goal: Smarter product binning by quality 0

50

100

150

200

0% 20% 40% 60% 80% 100%

Fal

lou

t (D

PP

M)

% of Chip Volume Sent to Burn-in

Trade-Off Improves with:• More of the RIGHT data• More chip volume

Model Pipeline Automation Driven by Workflow


PDF Implementation of workflow drives ML models in automation

Activates provide powerful automation of

analyses

Graphical layout allows users to visualize automation and

analysis flow.

Detailed configuration of activates provides power

analysis capabilities.

Smart Testing – Input Data


SORT:Yield + Parametric Data

WAT/PCM:Parametric Data

Data science / model prediction pipeline


unsupervised learning models to cluster data

Data science / model prediction pipeline


Based on inputs to group and label clusters

Fails Burn-In

Fails Burn-In

Good Chip Pass Burn-In

ReliabilityRMAScrap

ReliabilityReliability

Reliability

Before TIBCO Partnership


o Before partnership (started in 2007)

o PDF Developed all Infrastructure.

o Required wide focus:

– Charting / graphics

– Statistics

– Reporting

– Automation

– ….

– ….

– Semiconductor Applications

Visualizations

Charting

Analytics

Process Characterization

Semi ManufacturingKnow-how

Reporting

Automation Statistics

Value delivered to customer

TIBCO PDF Partnership


Interactivity

Statistics

TERRVisualizations

Charting

Semiconductor-specificAnalytics

ML Models

Unique Data

Process Characterization

Semi ManufacturingKnowhowReporting

Automation

AdvancedAnalytics

DataScience

Solutions

Big Data

Combined value delivered to customer!

+

10+ year partnership enables customers to leverage world class visualizations, big data analytics, and deployed infrastructure to realize Industry 4.0 vision.

Thank You

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cognitive end to end analytics for semiconductor manufacturing: … · 2019-11-07 · tibco pdf...

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