A closed loop dynamic scheduling method based on load balancing for semiconductor wafer fabrication facility

2018 ◽  
Author(s):  
Meiji Cui ◽  
Li Li
Keyword(s):  
Load Balancing ◽  
Closed Loop ◽  
Dynamic Scheduling ◽  
Wafer Fabrication ◽  
Semiconductor Wafer ◽  
Semiconductor Wafer Fabrication ◽  
Scheduling Method

Kohonen Neural Network – Based Performance Improvements for Wafer Photolithography Process with CONWIP Control Strategy

2010 ◽  
Vol 44-47 ◽  
pp. 18-22
Author(s):  
Bing Hai Zhou
Keyword(s):  
Neural Network ◽  
Dynamic Scheduling ◽  
Learning Algorithm ◽  
Wafer Fabrication ◽  
Semiconductor Wafer ◽  
Combination Rules ◽  
Performance Improvements ◽  
Kohonen Neural Network ◽  
Semiconductor Wafer Fabrication ◽  
Photolithography Process

Photolithography is usually the bottleneck process with the most expensive equipment in a semiconductor wafer fabrication system. To improve the performances of the photolithography area with dynamic combination rules, a method of Kohonen neural network (KNN)–based performance improvements is proposed. First, a dynamic scheduling framework based on a KNN model and scheduling rules is proposed. A KNN-based sample learning algorithm for improving the performances is presented. Finally, to demonstrate the validity and feasibility of the proposed method, data from a real wafer fabrication system are used to simulate the proposed method. Results of simulation experiments indicate that the proposed method can be used to improve a complex wafer photolithography performance.

A Swarm Intelligence Based Rescheduling Method for Semiconductor Wafer Fabrication Facilities

2011 ◽  
Vol 48-49 ◽  
pp. 123-126 ◽  
Author(s):  
Li Li ◽  
Fei Qiao
Keyword(s):  
Swarm Intelligence ◽  
Wafer Fabrication ◽  
Semiconductor Wafer ◽  
Fab Model ◽  
Production Environments ◽  
Semiconductor Wafer Fabrication ◽  
Scheduling Method ◽  
Scheduling Plan ◽  
Real Time Information ◽  
Time Information

An effective rescheduling method takes an important role on improving the operational performance of a semiconductor wafer fabrication facility (fabs). In this paper, we propose a rescheduling method based on swarm intelligence. Firstly, we build a swarm intelligence based rescheduling model (SIRM) including an ant queen agent, multiple job ant agents and machine ant agents. Secondly, we design a rescheduling algorithm (CMRA) composed of three sub-algorithms: sub-algorithm-1 is used by an ant queen agent to transfer an existing static optimized scheduling plan into additional pheromones of job ant agents; sub-algorithm-2 and sub-algorithm-3 are used to convert scheduling related real-time information to dynamic pheromones of job ant agents and machine ant agents, respectively. Finally, a simplified semiconductor wafer fab model is used to verify and validate CMRA. The simulation results demonstrate that CMRA is superior to the original scheduling method to generate a static optimized scheduling plan with better performance on move, step and on-time operational due date rate under uncertain production environments.

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