Big Data Analytics and AI for Smart Manufacturing in Semiconductor Industry

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1 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

2 Smart Manufacturing = Industry 4.0 Digital Supply Networks 4 th Industrial Revolution Electric Automation 1970s to 2000s Industrialization Start of the 20th century Power Generation Late 18th century Steam engines and hydraulic power drive improved productivity and enabled industrialization Electricity and assembly lines paved the way for mass manufacturing, improved infrastructure, and advances in financing and credit markets Advances in computing and the internet allow for information to be captured and transferred more quickly than ever before Execution of connected products, customers, and supply chain and operations - driven by a vast network of cyberphysical systems Optimize Traditional Objectives Cost Innovation Service Quality Safety Flexibility Source: Deloitte research and New Objectives Revenue By Better Managing Visibility Variability Volume Velocity 2

3 Smart Manufacturing Vision in Semiconductor Industry DIGITAL TWIN KNOWLEDGE NETWORK CYBER-PHYSICAL SYSTEMS BIG DATA Infrastructure VIRTUAL FACTORY MANUFACTURING Tool, Process, Yield & Facility Data Subject Matter Expertise (SME) SUPPLY CHAIN NETWORK Smart Manufacturing requires integration of Big Data Analytics, Knowledge Network & Digital Twin 3

4 Smart Manufacturing Challenges in Semiconductor Industry Online Behavior Data In Motion * 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 Available via: Solution Experts Equipment & Process Complexity Multivariate interactions Strong context data sensitivity Long data archives needed Data clustering and pre-treatment Process Dynamics Process drift/shift and variability Complicated maintenance practices Model portability and maintenance Other Issues Poor data quality Poor process visibility Data and IP security Tool Experts Big Data Analytics Solutions Overall Need Process Experts Incorporating Subject Matter Expertise (SME) in models 4 Data driven analytics by itself cannot address challenges in semiconductor industry

5 Big Data in Semiconductor Manufacturing Increase in Sophistication of Process Tools No. of Process Steps Grows 3x with Nodes Quadrillions of Data Points by mm Single tool 50KB data per second per tool 1,200 1, x 40,000 Others (WIP, CMP, Design etc.) Etch Litho Dep Defect and Metrology >500 tools per fab* ~1PB annual data per fab 1,000,000,000,000, / Nodes Progression (nm) 2D Device Architectures 3D Device Architectures * 30K wspm fab Source: VLSI Research & Applied Materials How to Make Sense of the Big Data? 5

6 Bottom Tuner Power Process Control Critical for Advanced Nodes Need techniques to control transitions in simplified manner 3D NAND PROCESS COMPLEXITY: Transitions for steps Short time 5 sec OX/ 15 sec NIT Film quality of ~ 250A OX/ 300A NIT Time Leading edge process requires highly repeatable control of process variations 6

7 Big Data & Computational Process Control (CPC) ANALYTICS Software, Modeling/Algorithms, Machine Learning DATA ENGINE Sensors, Metrology, Data Structure CPC (Computational Process Control) PROCESS Applications EQUIPMENT Design, Materials, Components Comprehensive Knowledge Network with SME is essential to making decisions based on Big Data 7

8 Building Secure, End-to-End Solution Inside Semiconductor Fab Inline Metrology Film thickness, CD, defects Secure Tool Connection Secure Remote Connection Applied Tools Data collection plans Sensors & in-situ analytics Field Service Server (FSS) Fleet Analytics High performance database Advanced analytics software Used by Applied process and equipment experts Increased collaboration increases customer benefit Knowledge Network Process, equipment, analytics and yield expertise Big Data infrastructure and algorithms 8

9 Virtual Sensors: Concept Examples Clean end point Wafer temperature Liquid line clogging Equipment Sensor + Physics-Based Models = Virtual Sensors Leverage Applied Materials domain knowledge + data analytics to provide new capabilities 9

10 Machine Learning Leveraging SME PROBLEM SOLUTION RESULT & BENEFIT Advanced nodes requiring tighter process control Excursion risks between metrology monitors Machine Learning application to predict WtW and WiW Deposition or Etch metrology results using tool sensor signals and advanced algorithms for each wafer run Process tuning/control for WtW / WiW Excursion control with 100% sampling CoO reduction with reduced metrology monitor Wafer output increase Machine Learning enables WtW & WiW process control 10

11 AI to Improve CPC PROBLEM SOLUTION RESULT & BENEFIT False positives in identifying equipment health anomalies Complex setup required for traditional FDC methods Using machine learning and AI algorithms to identify anomalies based on sample of good runs Validated with multiple lab and high volume manufacturing datasets Automated setup Robust to subtle and large anomalies AI/Machine Learning techniques combined with SME: Next Horizon for CPC 11

12 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. 12

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