Beyond Full Automation Eliminating Manufacturing Jan Rothe

Transcription

1 Beyond Full Automation Eliminating Manufacturing Jan Rothe Senior Manager Factory Solutions / Manufacturing Technology

2 Agenda A look back at automation Current challenges What is smart manufacturing for us? Outlook & Closure

3 Manufacturing getting rid of it Manufacturing Putting your hands to use Execution of repetitive tasks High Cost, High Risk What we really need (and partially have) Repetitive quality Manageable complexity Low Cost, No Risk License CC BY-SA3.0 shared on wikipedia.org by World Imaging, 2009 License CC BY-SA3.0 Uploaded to wikia.org by user Professor_Voodoo License CC BY-SA3.0 Uploaded to wikia.org by user Xernoc

4 The nineties and early 2000s driving full automation eliminating repetitive task & reducing risk Tasks eliminated: Process Start/Stop, Data Collection (20K each per day) Material Transport (40K per day) Key Enabler Load Ports AMHS, FOUPs, Communication I/F Key Benefits Error-free recipe selection Better clean-room quality More reliable task execution

5 Achieving automation enabled scenario optimization LP1 LP1 LP1 LP1 LP1 LP2 Just-in-time loading of equipment Predictive AMHS moves (just-in time lot pick-up and delivery) Processing Processing Carrier Exchange Time Carrier Exchange Time Processing Unload Load Processing Processing Unload Load Processing Reduced Carrier Exchange Time Reduced Carrier Exchange Time Unload Unload Load Load Processing Processing Speculative Dispatching & Carrier Load Preparation Speculative Dispatching & Carrier Load Preparation Unload Request Prediction Starvation Prediction Predicted Carrier Exchange Window for LP1 4,0% 3,5% 3,0% 2,5% 2,0% 1,5% 1,0% 0,5% 0,0% Litho Cells: Throughput Improvement w/ Reduced CET - 30s - 60s - 90s Lot Size

6 So that was the end of MANUfacturing Or was it?

7 Achieving automation goal enabled prevention of (new) risks Tasks eliminated: Process/Tool error detection Process Tuning Key Enablers Equipment Automation System Theory Key Benefits Stabilization of processes Earlier detection of potential issues

8 # of daily repetitive tasks Task complexity Addressing old challenges makes space for new ones Task complexity increased sometimes manually addressing not possible Time

9 Increasing task complexity for engineering staff Hundreds of sensor parameters collected per day Hundreds of process & electrical parameters that could be influenced Hundreds of small reasons why WIP is not moving (quick enough) BUT HOW do we know which ones are relevant for anything? Trace Parameter Time Trend Electrical Parameter Distribution Aggregate Parameter Grouping

10 New ways for correlation search & ranking reduce complexity for process engineers Known Parameter of Interest Automated correlation engine Established Most Influencial Parameter Automated correlation engine Potential Cause Determination Chemical Flow Correllation Validation & Correction

11 Challenge for such new algorithms Be able to deal with lots of structured data Determine the right algorithms for correlation analysis

12 # of daily repetitive tasks Task complexity Addressing old challenges makes space for new ones Task complexity increased sometimes manually addressing not possible Time

13 Many Manual Tasks are directly influencing Fab KPIs (Capacity, Cycle Time) Enable a consistent priority ranking & common priority communication source to drive all manual tasks Operator Interface Task Effectiveness (Max resulting prioritized moves) Task Efficiency (Fast selection & execution) Task Automation (Gradual solidification of procedures and task automation) Execution Automation Operator Interface Workflow & Task Execution

14 As manual tasks remain a reality, operator interfaces need to be tailored towards Task Effectiveness Task Efficiency Work Prioritization Consistent task ranking Business rule based prioritization System determined ranking Workflow organization and optimization Situation at a Glance Consolidated source of actionable information Rapid assessment and planning for task handling Same Solution for Same Problem Standardized task management & execution Guide users to task based action handling systems Collaboration and Communication Transparent work assignment Plan, communicate and align on workflow

15 Operator Interface What the User Sees User-specific Configuration Coordination and Collaboration Task-oriented Actions Coordination and Collaboration Task-oriented Details Prioritized Task List

16 Operator Interface Behind the Scenes Task Prioritization Drivers WIP WIP Tool Move

17 Behind the Scenes Task Ranking Based on WIP Priority, Quality Requirements, Impact Task Lot Priority Other Block Lot.1 Prototype Inhibit.A Lot.2 Normal Lot.3 Normal Inhibit.B Lot.4 Timelink No Runpath Lot.5 Normal Lot.6 Priority Tool Down Inhibit.C Lot.7 Normal Lot.8 Normal Hold.A Lot.9 Rocket Task Rank Task 1 Inhibit.B 2 Inhibit.A 3 Hold.A 4 Inhibit.C

18 The next challenge: how to profit from more data (in essence it is the same story all over again) Execution of some tasks limit Fab Speed (Execution, Learning, ) Initially: manual lot transport & job setup Later: exception handling & scenario optimization Then: Manual Task identification & prioritization Now: identifying relevant correlations that we may not know exist Data is generated in such amounts, it is almost impossible to still know in advance which new aspects are relevant for what Number of potential algorithms to run on the data is increasing every months

19 Outlook & Current activities Store all types of data with minimal pre-filtering Enable unlimited storage to be able to learn later Enable multiple algorithms to be tried out GLOBALFOUNDRIES solutions Running several big data pilots Yield, WIP Flow, Equipment Performance Analytics Create productive solutions on structured and unstructured data Reduce or eliminate waste for most valuable assets: our employees

20 ?