제어로봇시스템학회:학술대회논문집
- 제어로봇시스템학회 2003년도 ICCAS
- /
- Pages.1305-1308
- /
- 2003
Prediction of plasma etching using genetic-algorithm controlled backpropagation neural network
- Kim, Sung-Mo (Department of Electronic Engineering, Sejong University) ;
- Kim, Byung-Whan (Department of Electronic Engineering, Sejong University)
- 발행 : 2003.10.22
초록
A new technique is presented to construct a predictive model of plasma etch process. This was accomplished by combining a backpropagation neural network (BPNN) and a genetic algorithm (GA). The predictive model constructed in this way is referred to as a GA-BPNN. The GA played a role of controlling training factors simultaneously. The training factors to be optimized are the hidden neuron, training tolerance, initial weight magnitude, and two gradients of bipolar sigmoid and linear functions. Each etch response was optimized separately. The proposed scheme was evaluated with a set of experimental plasma etch data. The etch process was characterized by a