Open AccessA Plasma‐Etching Process Modeling Via a Polynomial Neural Network
Author(s) -
Kim Dongwon,
Kim Byungwhan,
Park GwiTae
Publication year - 2004
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.04.0103.0103
Subject(s) - artificial neural network , plasma etching , etching (microfabrication) , polynomial , node (physics) , adaptive neuro fuzzy inference system , algorithm , materials science , process (computing) , photoresist , computer science , fuzzy logic , artificial intelligence , mathematics , engineering , fuzzy control system , nanotechnology , structural engineering , mathematical analysis , layer (electronics) , operating system
A plasma is a collection of charged particles and on average is electrically neutral. In fabricating integrated circuits, plasma etching is a key means to transfer a photoresist pattern into an underlayer material. To construct a predictive model of plasma‐etching processes, a polynomial neural network (PNN) is applied. This process was characterized by a full factorial experiment, and two attributes modeled are its etch rate and DC bias. According to the number of input variables and type of polynomials to each node, the prediction performance of the PNN was optimized. The various performances of the PNN in diverse environments were compared to three types of statistical regression models and the adaptive network fuzzy inference system (ANFIS). As the demonstrated high‐prediction ability in the simulation results shows, the PNN is efficient and much more accurate from the point of view of approximation and prediction abilities.