ETRI Journal

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지

A Plasma-Etching Process Modeling Via a Polynomial Neural Network

  • Kim, Dong-Won (Department of Electrical Engineering, Korea University) ;
  • Kim, Byung-Whan (Department of Electronic Engineering, Bio Engineering Research Center, Sejong University) ;
  • Park, Gwi-Tae (Department of Electrical Engineering, Korea University)
  • Received : 2003.08.29
  • Published : 2004.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

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.

Keywords

References

  1. IEEE Plasma Sci. v.29 no.2 Modeling Plasma Equipment Using Neural Networks Kim, B.;Park, G.
  2. IEEE Trans. Plasma Sci. v.30 no.2 Characterization of Inductively Coupled Plasma Using Neural Networks Kim, B.;Park, S.
  3. IEEE Trans. Semicond. Manufact. v.6 no.4 Use of Neural Networks in Semiconductor Manufacturing Processes: An Example for Plasma Etch Modeling Rietman, E.;Lory, E.
  4. J. Applied Physics v.93 no.1 Qualitative Modeling of Silica Plasma Etching Using Neural Network Kim, B.;Kwon, K.H.
  5. IEEE Trans. Plasma Sci. v.30 no.2 Qualitative Fuzzy Logic Model of Plasma Etch Process Kim, B.;Park, J.
  6. Trans. KIEE v.52D no.7 Optimization of Polynomial Neural Networks: An Evolutionary Approach Kim, D.W.;Park, G.T.
  7. Trans. KIEE v.49D A Study on the Optimal Design of Polynomial Neural Networks Structure Oh, S.K.;Kim, D.W.;Park, B.J.
  8. IEEE Trans. Syst. Man Cybern. Polynomial Theory of Complex Systems Ivahnenko, A.G.
  9. Design and Analysis of Experiments Mongomery, D.C.
  10. IEEE Trans. Syst. Man Cybern. v.23 no.3 ANFIS: Adaptive-Networks-Based Fuzzy Inference System Jang, J.S.