论文信息 - Control relevant RIE modeling by neural networks from real time production state sensor measurements

Control relevant RIE modeling by neural networks from real time production state sensor measurements

In the present paper we address the problem of control relevant process modeling from production data for the n-well reactive ion etching processed by LAM Rainbow Etchers. Due to physical constraints we consider building an empirical neural network model using one lot of data which usually contains 24 wafers. Using the existence result of feedforward networks as universal approximators, we experimentally developed different network structures as models of the etching process under investigation. Our results are built upon extensive simulations on different lots of the process.

J. Si | Yuan-Ling Tseng

[1] Jennie Si,et al. A Systematic and Effective Supervised Learning Mechanism Based on Jacobian Rank Deficiency , 1998, Neural Computation.

[2] Edward A. Rietman,et al. A neural network model of a contact plasma etch process for VLSI production , 1996 .

[3] G. S. May,et al. Real-time predictive control of semiconductor manufacturing processes using neural networks , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[4] B. Chapman,et al. Glow Discharge Processes: Sputtering and Plasma Etching , 1980 .

[5] Costas J. Spanos,et al. Prediction of wafer state after plasma processing using real-time tool data , 1995 .

[6] S. Wolf,et al. Silicon Processing for the VLSI Era , 1986 .