By Topic

Using neural networks with a linear output neuron to model plasma etch processes

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Byungwhan Kim ; Dept. of Electron. Eng., Sejong Univ., Seoul, South Korea ; Wookyung Choi ; Hyegun Kim

A backpropagation neural network (BPNN) has been applied to model various plasma processes. As a neuron activation function, the BPNN typically uses either a bipolar or unipolar sigmoid function in both hidden and output layers. In this study, a BPNN with a linear function in the output layer and a bipolar sigmoid function in the hidden layer is introduced and applied to model a plasma etch process. The gradients related to the functions were experimentally optimized. The BPNN with a linear function in the output layer demonstrated an improved prediction over the BPNNs with other function combinations. By optimizing the gradients, its prediction accuracy was further significantly increased as compared to nonoptimized models. The process modeled is a magnetically enhanced reactive ion etch (MERIE) process and etch responses modeled include an etch rate, Al(Si) selectivity to photoresist, anisotropy and bias in critical dimension. The process was characterized by a 26$fractional factorial experiment

Published in:

Industrial Electronics, 2001. Proceedings. ISIE 2001. IEEE International Symposium on  (Volume:1 )

Date of Conference: