Title :
A study on ℜm→ℜ1 maps: application to a 0.16-μm via etch process endpoint
Author :
Rietman, Edward A. ; Layadi, Nace
Author_Institution :
Lucent Technol. Bell Labs., Orlando, FL, USA
fDate :
11/1/2000 12:00:00 AM
Abstract :
We introduce several endpoint algorithms that map real-time, in situ process signals to a via etch process endpoint. Some of the mathematical techniques include: Andrews plots (Fourier series), Chebyshev polynomials, Legendre polynomials, wavelets, singular value decomposition, and neural networks. We show that many of the techniques work to varying degrees of success for a via etch process on 0.16-μm technology. Based on our observations from many lots of manufacturing wafers and experiments with all the endpoint methods, we believe the Chebyshev polynomial area-time curves perform the best, but this statement should be taken with a caveat. It is really best to empirically test the various methods for a given etch process to deduce the endpoint algorithm for that application
Keywords :
Legendre polynomials; electronic engineering computing; etching; neural nets; polynomial approximation; semiconductor process modelling; signal processing; singular value decomposition; stochastic processes; wavelet transforms; 0.16 mum; 0.16-μm via etch process; Andrews plots; Chebyshev polynomial area-time curves; Fourier series; Legendre polynomials; endpoint algorithms; in situ process signals; neural networks; real-time; singular value decomposition; via etch process endpoint; wavelets; Etching; Motion pictures; Neural networks; Plasma applications; Plasma chemistry; Self-organizing networks; Shape; Signal mapping; Signal processing; Supervised learning;
Journal_Title :
Semiconductor Manufacturing, IEEE Transactions on
