Symbiotic Simulation Control in Semiconductor Manufacturing Authors Heiko

Symbiotic Simulation Control in Semiconductor Manufacturing Authors: Heiko Aydt, Stephen J. Turner, Wentong Cai, Malcolm Yoke Hean Low, Peter Lendermann, and Boon Ping Gan Presented by Heiko Aydt Parallel and Distributed Computing Centre Nanyang Technological University Singapore

Outline l l l Background Symbiotic Simulation and DDDAS Symbiotic Simulation Control System (SSCS) Wet Bench Tool Set (WBTS) Conclusions and Future Work 2

Background: Semiconductor Manufacturing l Semiconductor Manufacturing is ¡ ¡ Highly complex: several hundred processing steps and up to three month for production [1] Asset intensive: a single tool can cost up to US$ 2 million [2] l Ongoing improvement of the manufacturing process is crucial to stay competitive l Integrated automation solutions [3] ¡ ¡ Investment range between US$ 130 million and US$ 180 million Include real-time control of equipment 3

Background: Challenges and Need for Symbiotic Simulation l Decisions regarding tool configuration is made by engineers and carried out by workers l Problems: ¡ ¡ Mostly based on experience Workers don’t always strictly follow the instructions l Demand for fully automated control solutions l Symbiotic simulation control is such a solution 4

Symbiotic Simulation and DDDAS Physical System Implement Measure “What if” experiments simulate Output Analysis simulate Optimizer simulate 5

Symbiotic Simulation and DDDAS l Symbiotic simulation and DDDAS are very similar paradigms which are overlapping to some extend l Symbiotic simulation ¡ ¡ l Close relationship between a physical system and a simulation system Concept of what-if analysis is essential in symbiotic simulation Multiple simulations (of what-if scenarios) are dynamically datadriven Control feedback aims to modify the physical system DDDAS ¡ ¡ Not necessarily limited to dyamically data-driven simulations Control feedback aims to steer the measurement process 6

Symbiotic Simulation Control System (SSCS) l Symbiotic Simulation Control System (SSCS) ¡ ¡ l An agent-based generic framework for symbiotic simulation systems has been developed ¡ ¡ l An SSCS evaluates decision alternatives (e. g, alternative configurations) by means of simulation The ‘best’ decision is directly implemented in the physical system using corresponding actuators Reference implementation using Jadex/JADE Applicable in application scenarios of DDDAS/Symbiotic Simulation Framework provides various functional components ¡ ¡ ¡ Implemented as agent capabilities [4, 5] Standard implementations are provided Allows flexible design of application-specific symbiotic simulation systems 7

Symbiotic Simulation Control System (SSCS) Various Capabilities (x-C): l l S-C: Sensor WORC-C: Workflow Control SCEM-C: Scenario Management SIMM-C: Simulation Management l l l SIMA-C: Simulation Analysis DECM-C: Decision Management A-C: Actuator 8

Wet Bench Tool Set (WBTS) l A wet bench is used to clean wafers after certain fabrication steps l A wet bench consists of a number of baths with chemical liquids l Wafers lots are processed strictly according to recipes l Several wet benches with different setups are typically operated Wet. Bench Bath 1 Bath 2 Batch 1 Lot Bath 3 Batch 2 Lot Bath 4 Batch 3 Lot 9

Wet Bench Tool Set (WBTS) l Particles of contaminants are introduced into the baths during the cleaning process [6] ¡ ¡ l l l Some recipes introduce particles faster than others We therefore distinguish between ‘clean’ and ‘dirty’ recipes Wafers are processed in different wet benches depending on whether they are ‘clean’ or ‘dirty’ Thus, a wet bench operates either in ‘clean’ mode or ‘dirty’ mode Switching the operation ¡ ¡ From ‘clean’ to ‘dirty’: does not require any activity From ‘dirty’ to ‘clean’: requires a complete change of liquids 10

WBTS: Control Approaches Performance depends on product mix and operation modes of wet benches l ¡ Reconfiguration required if product mix is changing Common practise approach: l ¡ ¡ Engineers make decisions based on experience: difficult to model Following heuristic used: 1. 2. 3. 4. If the number of pending lots is exceeds limit: determine the critical recipe Identify all wet benches which are capable of processing the critical recipe but which are not configured yet Reconfigure one of the wet benches and wait some time (settling-in period) If, after the settling-in time, the situation did not improve: repeat SSCS approach: l ¡ ¡ ¡ Observes queues and trigger what-if analysis if number of pending lots exceed limit Create and evaluate a number of alternative configurations Determine best configuration implement it in the physical system 11

WBTS: Experiments l Emulator was used instead of a real physical system ¡ ¡ l Paced simulation Runs 3600 times faster than real-time Two kinds of experiments performed 1. 2. Alternating product mix High workload 12

WBTS: Experimental Results for Alternating Product Mixes Two different product mixes were used with constant load l ¡ The product mix is changed every 2 weeks Figure 1: Cycle Time of the WBTS over a period of 20 weeks using the SSCS (left) and the common practise control approach (right). SSCS produces a more homogeneous performance with l ¡ ¡ Less variability Lower mean cycle time Disadvantage of the common practise approach: l ¡ Needs several attempts to find a stable configuration 13

WBTS: Experimental Results for High Workload At high load, it is necessary to oscillate between configurations l ¡ ¡ Only two wet benches have a bath setup which provides many recipes At high loads, these two wet benches become a bottleneck Figure 2: Cycle Time of the WBTS over a period of one month using a load of 1000 (left) and 1200 (right) lots per day. l l Common practise approach becomes unstable when using 1200 lots per day SSCS can handle both loads without problems 14

WBTS: Experimental Results for High Workload Figure 3: Cycle Time of the WBTS over a period of one month using a load of 1500 (left) and 1700 (right) lots per day. The SSCS can handle 1500 lots per day but becomes unstable for 1700 lots per day Critical loads l l ¡ ¡ l For common practise approach: 1000 -1200 lots per day For SSCS approach: 1500 -1700 lots per day Performance improvement of 25 -70% when using the SSCS approach 15

Conclusions and Future Work l A dynamic data-driven application, more particularly a symbiotic simulation system, has been used to for real-time control of semiconductor manufacturing equipment l Our results show that using the SSCS yields a notable performance improvement over common practise l Symbiotic simulation is therefore a promising candidate for integrated automation solutions l Future work will be concerned with the application of an SSCS to an entire semiconductor factory 16

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