Big Data Analytics and AI for Smart

Manufacturing in Semiconductor Industry

Kirk Hasserjian

Corporate Vice President | Applied Global Services | Applied Materials

November 13, 2018

SEMICON Europa 2018

External Use

Smart Manufacturing = Industry 4.0

Source: Deloitte research

2

Power Generation

Industrialization

Electric Automation

Late 18th century

Start of the 20th century

1970s to 2000s

Steam engines and hydraulic

power drive improved

productivity and enabled

industrialization

Advances in computing and

the internet allow for

information to be captured

and transferred more quickly

than ever before

Electricity and assembly lines

paved the way for mass

manufacturing, improved

infrastructure, and advances

in financing and credit

markets

Execution of connected

products, customers, and

supply chain and

operations - driven by a

vast network of cyber-

physical systems

Optimize Traditional Objectives…Cost Innovation Service

Quality Safety Flexibility

4th Industrial Revolution

Digital Supply Networks

…By Better ManagingVisibility Variability

Volume Velocity

…and New Objectives…

Revenue

External Use

Smart Manufacturing Vision in Semiconductor Industry

3

VIRTUAL FACTORY

CYBER-PHYSICAL

SYSTEMS

SUPPLY CHAIN

NETWORK

MANUFACTURING

BIG DATA Infrastructure

DIGITAL TWIN KNOWLEDGE NETWORK

Tool, Process, Yield & Facility DataSubject Matter

Expertise (SME)

Smart Manufacturing requires integration of Big Data Analytics, Knowledge Network & Digital Twin

External Use

Smart Manufacturing Challenges in Semiconductor Industry

4

Data driven analytics by itself cannot address challenges in semiconductor industry

Process Dynamics

▪ Process drift/shift and variability

▪ Complicated maintenance practices

▪ Model portability and maintenance

Online BehaviorData In Motion

Big Data

Analytics

Solutions

Tool

ExpertsProcess

Experts

Solution

Experts

Other Issues

▪ Poor data quality

▪ Poor process visibility

▪ Data and IP security

Overall Need

Incorporating Subject Matter

Expertise (SME) in models

Equipment & Process Complexity

▪ Multivariate interactions

▪ Strong context data sensitivity

▪ Long data archives needed

▪ Data clustering and pre-treatment

* From: Moyne, J. and Banna, S., Beyond Traditional

Advanced Process Control: APC in Smart Manufacturing

(invited) e-Manufacturing & Design Collaboration Symposium 2018, Hsinchu, Taiwan,

September 2018. Available via: http://http://www.tsia.org.tw/seminar/eManufacturing2018/

External Use5

Big Data in Semiconductor Manufacturing

1,000,000,000,000,000

300mmSingle tool

>500tools per fab*

50KBdata per second

per tool

~1PBannual data per fab

How to Make Sense of the Big Data?

0

600

800

1,000

1,200

14/1620284565 10 7 5

3x

Nodes Progression (nm)

3D Device

Architectures2D Device

Architectures* 30K wspm fab Source: VLSI Research & Applied Materials

20182006

Increase in Sophistication of

Process Tools

No. of Process Steps

Grows 3x with Nodes

Quadrillions of Data Points

by 2018

0

40,000

Defect and Metrology

Dep

Litho

Etch

Others (WIP, CMP, Design etc.)

External Use

Process Control Critical for Advanced Nodes

6

3D NAND PROCESS COMPLEXITY:

▪ Transitions for 50-100 steps

▪ Short time 5 sec OX/ 15 sec NIT

▪ Film quality of ~ 250A OX/ 300A NIT

Time

Bo

tto

m T

un

er

Po

we

r

Need techniques to control transitions in simplified manner

Leading edge process requires highly repeatable control of process variations

External Use

Big Data & Computational Process Control (CPC)

7

CPC(Computational Process Control)

ANALYTICSSoftware, Modeling/Algorithms,

Machine Learning

EQUIPMENTDesign, Materials, Components

DATA ENGINESensors, Metrology, Data

Structure

PROCESSApplications

Comprehensive Knowledge Network with SME is essential to making decisions based on Big Data

External Use8

Building Secure, End-to-End Solution

Secure Tool

Connection

Applied Tools

Fleet Analytics

• High performance database

• Advanced analytics software

• Used by Applied process

and equipment experts

• Process, equipment,

analytics and yield expertise

• Big Data infrastructure and

algorithms

• Data collection plans

• Sensors & in-situ analytics

Knowledge Network

Secure

Remote

Connection

Inline Metrology

• Film thickness, CD, defects

Field Service Server

(FSS)

Inside Semiconductor Fab

Increased collaboration increases customer benefit

External Use

Virtual Sensors: Concept

9

Equipment Sensor + Physics-Based Models = Virtual Sensors

Examples

▪ Clean end point

▪ Wafer temperature

▪ Liquid line clogging

Leverage Applied Materials domain knowledge + data analytics to provide new capabilities

External Use10

Machine Learning Leveraging SME

▪ Advanced nodes requiring tighter

process control

▪ Excursion risks between metrology

monitors

▪ Process tuning/control for WtW / WiW

▪ Excursion control with 100% sampling

▪ CoO reduction with reduced metrology

monitor

▪ Wafer output increase

PROBLEM SOLUTION RESULT & BENEFIT

▪ Machine Learning application to predict

WtW and WiW Deposition or Etch

metrology results using tool sensor

signals and advanced algorithms for

each wafer run

Machine Learning enables WtW & WiW process control

External Use

AI to Improve CPC

11

▪ False positives in identifying

equipment health anomalies

▪ Complex setup required for traditional

FDC methods

▪ Validated with multiple lab and high

volume manufacturing datasets

▪ Automated setup

▪ Robust to subtle and large anomalies

PROBLEM SOLUTION RESULT & BENEFIT

▪ Using machine learning and AI

algorithms to identify anomalies based

on sample of good runs

AI/Machine Learning techniques combined with SME: Next Horizon for CPC

External Use12

Summary

▪ Smart Manufacturing (= Industry 4.0) in semiconductor industry requires integration of big

data analytics, knowledge network and digital twin.

▪ As data explodes in semiconductor manufacturing with node advancement, process

control becomes even more critical.

▪ Applied Material’s Computational Process Control (CPC) solutions address big data

challenges in semiconductor smart manufacturing, with knowledge network incorporating

Subject Matter Expertise (SME).

▪ Increased collaboration with customers with strong data security is critical to fully realize

process control benefit for advanced nodes.