Variability Control A Key Challenge and Opportunity for Driving Towards Manufacturing Excellence

Transcription

1 James Moyne, Ph.D. Applied Materials, Applied Global Services University of Michigan, Associate Research Scientist ITRS, Factory Integration (FI) Technical Working Group Chair Variability Control A Key Challenge and Opportunity for Driving Towards Manufacturing Excellence July 8th, 2014

2 New Factory Integration (FI) Vision Prediction Roadmap Big Data Roadmap Summary

3 Emphasize commonality among areas Emphasize integration of FI capabilities across the fab Emphasize the move to a data driven approaches Consider the impact of non-nanomanufacturing Consider that fundamentally, 300mm and 450mm are the same for Factory Integration in many areas 3

4 New Thrusts Augmenting Reactive with Predictive Big Data Control Systems Architectures FI TWG Thrust Teams UI Factory Operations Production Equipment AMHS Factory Information & Control Systems Facilities Major Revision of Factory Integration Chapter New thrust areas New ITRS sub chapters 4

5 Prediction Vision: Yield and throughput prediction is an integral part of factory operation optimization Real-time simulation of all fab operations occurs as an extension of existing system with dynamic updating of simulation models Predictive technologies Predictive Maintenance (PdM) Equipment Health Monitoring (EHM) Virtual Metrology (VM) Predictive Scheduling Predictive Yield 5

6 Year of Production DRAM ½ Pitch (nm) (contacted) Predictive Scheduling: fab-wide solutions Wafer Diameter (mm) EHM: fab-wide solutions (common health indicator across tools) PdM: fab-wide solutions Partial (litho primary) Partial Partial Partial All All Minimal Partial Partial Partial All All None None Minimal Minimal Partial Partial VM: fab-wide solutions Yield Prediction: fab-wide solutions None None Minimal Minimal Partial Partial None None Minimal Minimal Partial Partial Simulation as an extension of existing systems with real-time update: all-systems, fab-wide KEY None None None None Minimal Minimal Solutions Exist Solutions Are Known Solutions Are Not Known 6

7 First Year of IC Production General Comprehensive prediction roadmap (including standards for integration of prediction technologies, making existing capabilities such as maintenance systems "prediction ready", establishing data requirements for prediction systems, etc.). Equipment Health Monitoring (EHM) Standardized EHM dashboard design EHM library component models defined for each tool type KEY Research Required Development Underway Qualification / Pre production Continuous Improvement 7

8 First Year of IC Production Predictive Maintenance (PdM) Standards for PdM capabilities, interface, data quality, and maintenance system interface for PdM. Methods for PdM prediction quality determination, represen and optimization Predictive Scheduling Methodologies for lithography predictive scheduling with integration to real-time scheduling and dispatch Methodologies for predictive scheduling in non-lithography areas Methodologies for area and fab-wide predictive scheduling and optimization KEY Research Required Development Underway Qualification / Pre production Continuous Improvement 8

9 Virtual Metrology Standards for virtual metrology capabilities and interfaces Re-usable VM methods for smart metrology to support across- fab implemen Re-usable VM methods to support process control, NPW reduction, PM recovery, etc. Predictive Yield Methods for effective yield prediction and yield prediction use given factors such as fab data quality Standards for specification and integration of yield prediction solutions KEY Research Required Development Underway Qualification / Pre production Continuous Improvement 9

10 Data generation storage and usage are increasing in nanomanufacturing Issues with this data explosion Volume: Velocity: Variety: Veracity: Value: Amount of data Data collection and storage rates Multiple data sources and data merging Data quality & data quality requirements Methods for getting most out of the data 10

11 Table FAC12 Big Data Requirements Year of Production DRAM ½ Pitch (nm) (contacted) Wafer Diameter (mm) Volume BD Requirements: Daily data collection volume across fab (Terabytes) TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD Minimum data retention in fab: Maintenance Data TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD (Months) Minimum data retention in fab: Diagnostic Output TBD TBD TBD TBD TBD TBD TBD TBD TBD Data Data retention in fab: Trace Data TBD TBDLets TBDbreak down TBD TBD TBD this TBD TBDeye TBD chart TBD TBD TBD Minimum data retention in fab: Process Control and Metrology Data TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD Minimum data retention in fab: Yield Data TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD Minimum data retention in fab: Execution Log Data TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD Minimum data archive length: Maintenance Data (Years) TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD Minimum data archive length: Diagnostic Output Data TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD Minimum data archive length: Trace Data TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD Minimum data archive length: Process Control and Metrology Data TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD Minimum data archive length: Yield Data TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD Minimum data archive length: Execution Log Data TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD Velocity BD Requirements: FICS design to support peak equipment data transfer rates (production rate for each variable) FICS design to support peak factory data transfer rates (Bytes / second) Variety BD Requirements: Relative accuracy of mission critical FICS clocks to fab-level time authority 10Hz 10HZ 100Hz 100Hz 100Hz 100Hz 100Hz 100Hz 1kHz 1kHz 1kHz 1kHz > 1kHz > 1kHz > 1kHz > 1kHz 160 khz 500kHz >1.6 >2 >2 >2 >10 >10 5ms 5ms 1ms 1ms 1ms 1ms 1ms 1ms 1ms 1ms 1ms 1ms > 1ms > 1ms > 1ms > 1ms Time accuracy of human entered data TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD Standards to support automatic merging of data stores (Maintenance, Diagnostic ouptut, Trace, Process Control/Metrology, Yield and Execution Minimal Minimal Partial Partial Partial Partial Full Full Full Full Full Full Full Full Full Full Log) across FI space Veracity BD Requirements: Specification of the data quaility of data stores per data quality standard metrics Minimal Minimal Partial Partial Partial Partial Full Full Full Full Full Full Full Full Full Full Value BD Requirements: Standardized mechanism to determine benefit of data, e.g., in $ / TeraByte of storage Cloud and Integration BD Requirements: Enterprise-wise Integration of fab and facility data stores None None Developed Developed Partial Partial Full Full >16 Full >16 Full >16 Full >16 Full >16 Full >16 Full >16 Full TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD Performance of Data I/O to/from the cloud TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD Data integration up and down the supply chain TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD Standards for secure cloud data access NONE TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD TBD KEY Focusing on the categories Note: Many TBD s here A task for 2014 >16 Full Solutions Exist Solutions Are Known Solutions Are Not Known 11

12 Volume BD Requirements: Daily data collection volume across fab (Terabytes) Minimum data retention and data archive length in fab Maintenance Data Diagnostic Output Data Trace Data Process Control and Metrology Data Yield Data Execution Log Data Velocity BD Requirements: FICS design to support peak equipment data transfer rates (production rate for each variable) FICS design to support peak factory data transfer rates (Bytes / second) 12

13 Variety BD Requirements: Standards to support automatic merging of data stores (Maintenance, Diagnostic output, Trace, Process Control/Metrology, Yield and Execution Log) across FI space Relative accuracy of mission critical FICS clocks to fab-level time authority Time accuracy of human entered data Veracity BD Requirements: Specification of the data quality of data stores per data quality standard metrics 13

14 Value BD Requirements: Standardized mechanism to determine benefit of data, e.g., in $ / Terabyte of storage Cloud and Integration BD Requirements: Enterprise-wise Integration of fab and facility data stores Performance of Data I/O to/from the cloud Data integration up and down the supply chain Standards for secure cloud data access 14

15 First Year of IC Production Volume Velocity Capabilities in place to support required data retention both resident and archived Methods of data storage optimized to data analysis methods (e.g., via standardized data models) Solutions to support peak equipment and peak factory data transfer rates Lets break down this eye chart Focusing on the categories Variety Standards for data models of key FI data stores including maintenance, diagnostic, Process control/metrology, yield and Execution Log. Veracity Value Other Data quality baseline specifications provided as standardized metric values for key FI data stores including including maintenance, diagnostic, Process control/metrology, yield and Execution Log. Standardized mechanisms in place used to determine benefit of data, e.g., in $ / TeraByte of storage Communication standards, data models, high-speed integration methods, and security protocols in place to support Cloud Computing as a solution for FI systems Note that the roadmap is not developed yet A task for 2014 KEY Research Required Development Underway Qualification / Pre production Continuous Improvement 15

16 Volume Capabilities in place to support required data retention both resident and archived Methods of data storage optimized to data analysis methods (e.g., via standardized data models and Hadoop) Velocity Solutions to support peak equipment and peak factory data transfer rates Variety (big issue in our industry) Standards for data models or interfaces of key FI data stores including maintenance, diagnostic, Process control/metrology, yield and Execution Log. 16

17 Veracity (big issue in our industry) Data quality baseline specifications provided as standardized metric values for key FI data stores including maintenance, diagnostic, Process control/metrology, yield and Execution Log Value Efficient analytics and processing Standardized mechanisms in place used to determine benefit of data, e.g., in $ / Terabyte of storage Other Communication standards, data models, high-speed integration methods, and security protocols in place to support Cloud Computing as a solution for FI systems 17

18 Prediction and Big Data are major components of the new Factory Integration ITRS Roadmap ( Significant ITRS strides in 2013; work ongoing in 2014 ITRS ARP and BD Topics Narrative Technical Challenges roadmap Defined scope and issues Defined requirements categories No change Begin to quantify requirements into BD roadmap Potential Solutions Defined solutions Begin to define roadmap Other 2014 Efforts Education: Supporting a one-day Big Data tutorial for nano-manufacturing At APC Conference, September, www. apcconference.com Begin discussion of 2015 roadmap additions Security?? Supply Chain Integration?? Pumps Data Integration?? Analytics?? 18

19 ITRS FI members Contact me if you would like to be more involved in 2014 SEMATECH and Member Companies Big Data Surveys and specifications APC Conference XXVI Ann Arbor, Michigan, September 2014 Big Data Tutorials Thank you!! Daniel Babbs Brooks Automation Jeff Binford Imftech Steve Chadwick Intel Corporation Jonathan Chang Infinieon Peter Csatary M+W Group Mike Czerniak Edwards Vacuum Chad Durfee Applied Materials Boyd Finlay Sematech Masazumi Fukushima Murata Machinery Ltd. Avi Furest Intel Corporation Barbara Goldstein NIST David Hanny Applied Materials Chih-Wei (David) Huang TSMC Leo Kenny Intel Corporation Les Marshall GLOBALFOUNDRIES Supika Mashiro Tokyo Electron Daniel McCulley Intel Corporation Rick McKee Micron Technology Reiner Missale T Systems Steve Moffatt Applied Materials James Moyne Applied Materials Richard Oechsner Fraunhofer Institute Markus Pfeffer Fraunhofer Institute Sanjay Rajguru Sematech Gopal Rao Sematech Gavin Rider Independent Dan Stevens Hirata Corporation of America Brian Sweet Micron Technology Toshiyuki Uchino Renesas Alan Weber Cimetrix Makoto Yamamoto Murata Machinery Ltd. Hiroyuki Yasumoto Murata Machinery Ltd. Contact FI Chair for most up to date list 19

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21 Hadoop Distributed File System (HDFS) From: overview.php Other Industries are addressing many of the Vs for us 21

22 SEMATECH - Big Data Surveys - APC Council Parallel Efforts - NIST Big Data Architecture - Big Data Industry Solutions Big Data Research and Development ITRS FI Technology Working Group Roadmaps SEMI / SEMATECH Specifications Big Data Table: Technical Challenges Roadmap Big Data Table: Potential Solutions Roadmap Specifications for data store data quality (Veracity) Specifications for data store interfaces (Variety) APC Conferences, etc. Education Papers Case studies, requirements, issues Big Data Tutorials (APC US) 22