Title :
On-line learning delivery decision support system for highly product mixed semiconductor foundry
Author :
Yu, Chih-Yuan ; Huang, Han-Pang
Author_Institution :
Dept. of Mech. Eng., Nat. Taiwan Univ., Taipei, Taiwan
fDate :
5/1/2002 12:00:00 AM
Abstract :
A production learning system (PLS) based on the tool model was constructed as a decision support and real-time information update system to forecast the cycle time. A tool model includes a waiting model and a processing model. Each of the waiting and processing models uses a backpropagation neural network to establish the relationship between the input and output (time) of the model. Hence, cycle time estimation, tool group move and confirm line item performance (CLIP) value can be obtained based on the memory stored in the neural network. The result shows that the forecasting ability of the PLS has an error rate below 8% on average
Keywords :
backpropagation; decision support systems; error analysis; integrated circuit manufacture; manufacturing data processing; manufacturing resources planning; neural nets; production control; semiconductor process modelling; CLIP value; PLS; PLS error rate; PLS forecasting ability; backpropagation neural network; confirm line item performance value; cycle time estimation; cycle time forecasting; decision support/real-time information update system; high product mix semiconductor foundry; model input time; model output time; neural network stored memory; on-line learning delivery decision support system; processing model; production learning system; tool group move; tool model; waiting model; Backpropagation; Decision support systems; Delay estimation; Foundries; Learning systems; Neural networks; Nonlinear dynamical systems; Production systems; Robotics and automation; Semiconductor device modeling;
Journal_Title :
Semiconductor Manufacturing, IEEE Transactions on
