Actionable insight in semiconductor manufacturing, from big data to Cognitive Computing

Christophe Begue, beguec@us.ibm.com July 2016

Copyright 2016 IBM

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

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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