Actionable insight in semiconductor manufacturing, from big data to Cognitive Computing
Christophe Begue, [email protected] July 2016
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GPS
Semiconductor firms see significant opportuni2es for Big Data to op2mize the way they execute across func2ons
Product Development & Manufacturing … compress design, development & manufacturing lead time and improve yield and asset utilization
Marketing & Sales ... design and execute more effective marketing with optimized product assortments, affinities and pricing
Supply Chain & Distribution ... optimize inventory and assets and deliver a reduction in supply chain and distribution costs with single view product
Market Research & Product Ideation
... align product concepts with consumer desires, improve new product ideas, and new
product launch effectiveness for IoT
Procurement & Vendor Management ... embed insight into business processes from Manufacturer to Distributor to Customer to Consumer
Finance ...grow revenue and improve margins with greater business performance insight, and improved forecasting and planning
External Data
Massive Internal Data
Field and Warranty Management ... collect field data from connected devices, understand part behavior, predict failures, reduce warranty cost
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Traditional Fab Infrastructure
MES
Messaging Bus
Recipes Auto mation
Data collect
FDC Reports SPC APC YMS
Current Scope
Advanced Capabilities
Stop bad product or tool
Wafer / Lot Analysis Single parameter trends
Uni à Multivariate
Tool Sensor Diagnostics Predictive Maintenance
Virtual Metrology
Data Mining
Enhanced Data Mining Predictive Modeling
Automated Models
“Big Data”
Many fabs struggle to move from current capabili2es to advanced predic2ve techniques for yield and asset management
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Manufacturing, Product data in context of supply chain, consumers, quality, external events
Responding dynamically to multi dimensional and time sensitive situations
Applying machine learning techniques to interconnected physical devices data, patterns and trends
Marrying instrumented and interconnected data with unstructured data
Yet the growth of sensor and IoT data calls for a different approach
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Why is “Cogni2ve” necessary? • Data from billions of interactions between machines, devices and people is massive, complex
and variable.
• Pre-defined programs aren’t able to analyze it. Traditional systems can't make sense all the IoT data combined with unstructured data.
• A cognitive system makes sense of all types of data, it works across data sources and decides
which patterns and relationships matter.
• It uses machine learning and advanced processing to organize the data and generate insights. It evolves and improve through learned self-correction and adaptation.
ANALYTICS COGNITIVE INFORMATION Knowledge DATA SENSORS
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There are three capabili2es that differen2ate cogni2ve systems from tradi2onal programmed compu2ng systems
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Today Cognitive Manufacturing
Constraints in the past Data: Structured /historical data (e.g. wafer maps, process tools, etc) Analysis: Reactive / Preventive Maintenance Production Planning: Daily Cycle with managing demand
New Capabilities in the future Data: Structured /Unstructured / Stream (e.g. Adaptive Test) Analysis: Conditional to predictive Maintenance, Yield Analysis Production Planning: Real Time WIP planning and routing
Based on real-time prediction model and sensor data to diagnose the risk of EQP. 1 min
Based on Inspection data to discover the EQP with unstable performance. 6hrs
Engineer was informed to shutdown the EQP and started the process of trouble shooting. 4hrs
Products had been hold until Engineer recover EQP
Influenced product can't be reworked
Produce 30 lots to cover the lost of scrap, high priority product type was delay to deliver. 8hrs
EQP auto-shutdown and Analyzer Engine send root-cause ranking list for Engineer to fix the problem. 10 mins
Optimizer Engine to calculate the best dispatch scheduling, send decision to control center, enable backup EQP. 5 mins
Integrate information of influenced products to Analyzer Engine to generate revised recipe and revised route for next operation. 2 mins
high priority product type on time
Interconnec2vity, real 2me analysis, structured process and unstructured context data and machine learning are the essen2al elements of Cogni2ve Manufacturing
Copyright 2016 IBM
Connect & Configure
Develop & Visualize
Analyze & Predict
Become Cognitive
• Instrument your equipment/assets to collect data
• Gather already existing data
• Visualize your data in meaningful dashboards
• Start to see patterns
• Gain insights from the data
• Predictive models
• Add context data • Bring in cognitive
components • Machine learning • Understand,
Reason, Learn Asset needs to be connected, outfitted with sensor or data gathered
Use analytical models to predict equipment failures and
Use the platform to quickly build dashboards for data visualization
Use speech, video, image to diagnose complex problems
STAIRCASE TO COGNITIVE MANUFACTURING
Copyright 2016 IBM