Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Ähnlich wie Big Data LDN 2018: USING FAST-DATA TO MAKE SEMICONDUCTORS
Ähnlich wie Big Data LDN 2018: USING FAST-DATA TO MAKE SEMICONDUCTORS (20)
Mehr von Matt Stubbs
Mehr von Matt Stubbs (20)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
Big Data LDN 2018: USING FAST-DATA TO MAKE SEMICONDUCTORS
- 1. USING FAST DATA TO MAKE… SEMICONDUCTORS Neil Condon | 14 November 2018
- 2. ABOUT ME § Neil Condon (neil.condon@edwardsvacuum.com) § 15+ years working alongside leading edge semiconductor players § Responsible for understanding future customer needs § Advocate for the data-driven business – Technology and culture – New, valuable products and services § Contributor to the IRDS: Technology road-mapping – Factory Integration § Big data combined with subject-matter expertise § Data and IP security 2
- 3. ABOUT MY COMPANY § Global critical subsystems supplier to the industry – Vacuum equipment, for low-pressure processing, of all kinds – Gas treatment equipment, for safety and environmental stewardship § Wholly owned by Atlas Copco, since 2014 (~46,000 employees; ~US$12.7bn) § 6300 employees, globally § US$2.15bn revenue (2017) § Headquartered, with core R&D in the UK § Manufacturing in US, UK, Korea, China § Customers all over the world… 3
- 4. OUTLINE § Background to Semiconductor Manufacturing – Themes that define the industry § Role of Fast Data in Semi Manufacturing – What we know, and what’s missing § Example: semi-supervised learning from time-series § “Big Data” Semi Roadmap Challenges 4
- 5. OUTLINE § Background to Semiconductor Manufacturing – Themes that define the industry § Role of Fast Data in Semi Manufacturing – What we know, and what’s missing § Example: semi-supervised learning from time-series § “Big Data” Semi Roadmap Challenges 5
- 6. A SENSE OF SCALE, IN SEMI… 6 x 300,000 /month >100 sensors @ 10 Hz >1000x 1 min steps >1.1 GB / wafer >11 PB / day
- 7. COMPLEX AUTOMATED TRANSPORT & ROUTING 7 OperationA OperationB OperationC OperationD OperationE Product X Product Y
- 8. A COMPLEX SUPPLY CHAIN 8 Designs Fabless firms Completed wafers Fabs & Foundries Functioning “die” •Binned by performance Dicing & test Packaged “chips” Packaging “Final” Product Application Process design rules Device/location test results Performance distribution Equipment Process technology
- 9. 9 Sources: Price, Waterhouse, Coopers; Morgan Stanley (2017); Intl. Business Strategies (2017); IC Insights (2018) CAGR (1985-2012) 10.1% CAGR (2013-2018) 4.3%* Fab Costs ↑ 168% Process Development Costs ↑225% Chip Design Costs ↑341% Sector Revenue Growth Sector Cost Growth Sector Revenue Drivers Sector Cost Drivers 2017 - 20XX, SEMI MARKET SECTOR DYNAMICS Increasing complexity + need for SW/HW co-development 2017 -20XX: Operational Challenges for the Semiconductor Industry Since 2013: Tablet+Gaming shrinking; Automotive+IoT now significant
- 10. OUTLINE § Background to Semiconductor Manufacturing – Themes that define the industry § Role of Fast Data in Semi Manufacturing – What we know, and what’s missing § Example: semi-supervised learning from time-series § “Big Data” Semi Roadmap Challenges 10
- 11. 11 Semi manufacturing… an analogy
- 12. WHO RECOGNISES THIS GUY? 12
- 13. WHAT ABOUT THESE GUYS? 13 images © Lydia Monks, 2009
- 14. FOLLOWING THE PLAN… IN THE DARK 14 “In through the gate, at dead of night. Pass the horse, then turn right. Round the duck pond, past the hog… Be careful not to wake the dog! Left past the sheep, then straight ahead… And in thro’ the door of the Prize Cow’s shed!” images © Lydia Monks, 2009
- 15. THE SEMICONDUCTOR STORY… § 1000+ complex processes, in sequence – If all are executed correctly, we get functioning products. – If not – what went wrong? § It’s dark, and the torch batteries are fading… – More and more of the information that would confirm we’re on track, isn’t available. – It’s expensive and slow to actually look at the wafer. § We can’t see where we’re got to, so we “listen” for other indications – We try to do all the right things well, in the right order – We “listen” to the production equipment, for noises that confirm that all is well § Yield Management (YM) / Fault Detection & Classification (FDC) tools help us to identify problems – We use what we see as feedback/feed-forward to the factory automation § If these are inadequate, or mislead us, things can get messy… 15
- 16. HOW DO WE TRACK WHAT’S GOING ON? § Post-process inspection is slow § Inspection equipment is $$$ 16 iall i1 i2 i3 i1 i2 i3
- 17. OUTLINE § Background to Semiconductor Manufacturing – Themes that define the industry § Role of Fast Data in Semi Manufacturing – What we know, and what’s missing § Example: semi-supervised learning from time-series § “Big Data” Semi Roadmap Challenges 17
- 18. THE “FDC” PARADIGM § FDC engineers evaluate variability by monitoring time-series traces: 18 spike slope plateau damped oscillation exponential decay “control plan” = { parametric features }
- 19. THE “FDC” PARADIGM § FDC engineers evaluate variability by monitoring time-series traces: 19 Contextual triggers: e.g. signals from tools, or the MES
- 20. COMMON BUGBEARS OF FDC AT SCALE 20 Lots of process recipes, particularly in Foundry context Recipes evolve in R2R control paradigm Recipes adjusted in CIP of throughput or yield Control Plan CIP, to improve fault detection performance Different sites develop different CP for same process … § Faster creation of Control Plans – semi-supervised machine learning? § “Elastic” Control Plans – plans that adapt and “stretch” to programmed recipe variation, without explicit revision? § New methods of variability measurement, which are “schema-lite” – full sensor trace analytic? Desirable:
- 21. DISCOVERING TIME-SERIES “MOTIFS” IN REAL-TIME 21 = Motif ‘A’ = Recipe ‘W’ = Motif ‘B’ = Recipe ‘X’ = Motif ‘C’ = Recipe ‘Y’ = Motif ‘D’ = Recipe ‘Z'
- 22. MOTIF DISCOVERY - TECHNICAL CHALLENGES Why is this hard? § A priori, the number of motifs is unknown – Many general purpose clustering techniques, e.g. K-means can’t help § The length of motifs is unknown, and varies (e.g R2R) – You may have to try sub-sequences of all lengths, and different lengths § Distance computation (measuring similarity) is expensive – Euclidian distance – O(N); Dynamic-Time-Warping – O(N2), in general § In a real-time context it’s hard to summarise data to simplify the problem – Many techniques (e.g. normalisation) often rely on statistics of the whole TS, which we don’t have in real-time 22
- 23. EFFECTIVE ALGORITHMS ARE COMPLEX § ML pipelines use many stages, and leverages domain-knowledge in most subroutines 23 Real-time data Subsequence selection Known motifs & occurrences Matching algorithm Domain rules Last n motifs Expectation theory Match? Partial match? Common subsequence discovery Domain hyper- parameters N N Y Y Anomaly? Normal Advisory Adaptive control Product application knowledge Predictive maintenance algorithms Product application knowledge N Y
- 24. OUTLINE § Background to Semiconductor Manufacturing – Themes that define the industry § Role of Fast Data in Semi Manufacturing – What we know, and what’s missing § Example: semi-supervised learning from time-series § “Big Data” Semi Roadmap Challenges 24
- 25. IRDS CHALLENGES 25 The International Roadmap For Devices and Systems
- 26. “BIG DATA” & ANALYTICS REQUIREMENTS 26 YEAR OF PRODUCTION: 2018 2019 2020 2021 2022 2023 2024 2025 Velocity Big Data Requirements FICS design to support peak equipment data transfer rates (production rate for each variable). 10 Hz 100Hz 100Hz 1 kHz 1 kHz 1 kHz 1 kHz >1kHz FICS factory data transfer rates (Bytes / s) per 1000 tools in fab. >2 MHz >10 MHz >10 MHz >16 MHz >16 MHz >16 MHz >16 MHz >16 MHz Variety Big Data Requirements Standards to support automatic merging of data stores (Maintenance, Diagnostic output, Trace, Process Control, Yield and Execution Log) across FI space. Partial Partial Partial Full Full Full Full Full Value Big Data Requirements Enterprise-wide integration of fab and facility data stores TBD Performance of data I/O to/from the cloud TBD Data integration up and down the supply-chain TBD Standards for secure cloud data access TBD Migration to “Big Data friendly” ecosystems (e.g. Hadoop) Used for offline analysis and modelling Partial Partial Partial Full Full Full Full Full Used for “real-time” online diagnostics and control None Minimal Minimal Partial Partial Partial Partial Partial Reference: https://irds.ieee.org/roadmap-2017 Manufacturable solutions exist/are being optimised Manufacturable solutions are known Manufacturable solutions are NOT known
- 27. SECURITY ROADMAP YEAR OF PRODUCTION: 2018 2019 2020 2021 2022 2023 2024 2025 Security for data sharing Classification of data into Proprietary/Licensed/Shared/Public categories and establishment of Data Owners and Licensees Establishment of Distributed Trust mechanisms for Data Owners, Consumers and Autonomous Agents Establishment of non-repudiation, tamper detection, traceability and loss management features for data. Development of industry standards for each category of data in a fab and the standard security level for that category To balance data confidentiality and integrity with availability by partitioning data with IP protections and standardizing data encryption. Adoption of IT standards for identity and access management including human and non- human access, To facilitate central management on user accounts management throughout Fab including production equipment (may include single sign-on as appropriate) Security for Equipment Operation by the FICS Protection of the equipment's instrumentation and control systems from attack. IP protection capabilities and achieving balance between data availability and IP protection Security for Big Data and Leveraging Big Data for Security Security protocols in place to support Cloud Computing as a solution for FI systems Application of big data analytics to identifying security issues 27 Research required Development underway Qualification/ pre-production Continuous improvement To be determined Reference: https://irds.ieee.org/roadmap-2017
- 28. A SECURE PLATFORM THAT… § Spans on-premise, multi-premise and multi-cloud – Allow data to be moved between trusted parties – Allow cost-effective use of cloud services for analytics etc. § Allows access control policy to be administered and applied consistently, wherever the data are – Land the data in the fab – Transfer/replicate the data, for a purpose, to an equipment supplier, or the cloud. – Data owners can rescind access or delete copies of the data, wherever resides physically § Allows analytics to be built and deployed where it’s needed – Portable, orchestratable, life-cycle managed § Captures the valuable output from analytics – So that it can also be secured – So that it can be “consumed” in “real-time”, and used in factory automation 28 A Fast Data PaaS for Semi? Multi- premise Cloud Identity mgmt Big Analytics Small Analytics
- 29. 29 Should be easy, then……..