A study on ℜm → ℜ1 maps: Application to a 0.16-μm via etch process endpoint

IEEE Transactions on Semiconductor Manufacturing

Volumn 13, Issue 4, 2000, Pages 457-468

  Rietman, Edward A ^a   Layadi, Nace ^b  

^a Starlab NV   (Belgium)

^b NOKIA RESEARCH CENTER   (United States)

Author keywords

Andrews plots; Angles of vectors in hyperspace; Minkowski distance; Neural networks; Polynomials; Singular value decomposition; Stochastic resonance; Vector norms; Wavelets

Indexed keywords

ALGORITHMS; CONFORMAL MAPPING; FOURIER TRANSFORMS; MATRIX ALGEBRA; NEURAL NETWORKS; PLASMA STABILITY; POLYNOMIALS; RANDOM PROCESSES; STATISTICAL METHODS; VECTORS; WAVELET TRANSFORMS;

ANDREWS PLOTS; ANGLES OF VECTORS IN HYPERSPACE; CHEBYSHEV POLYNOMIAL; LEGENDRE POLYNOMIAL; MINKOWSKI DISTANCE; SINGULAR VALUE DECOMPOSITION; STOCHASTIC RESONANCE; VECTOR NORMS;

PLASMA ETCHING;

EID: 0034313463     PISSN: 08946507     EISSN: None     Source Type: Journal    
DOI: 10.1109/66.892632     Document Type: Article

References (14)

1. E. A. Rietman, J. T. C. Lee, and N. Layadi, "Dynamic images of plasma processes: Use of Fourier blobs for endpoint detection during plasma etching of patterned wafers," JVST-A, vol. 16, no. 3, pp. 1449-1453, 1998.
2. D. F. Andrews, "Plots of high-dimensional data," Biometrics, vol. 23, pp. 125-136, 1972.
3. J. M. Chernoff, "The use of faces to represent points in k-dimensional space graphically," J. Amer. Statist. Assoc., no. 68, pp. 361-368, 1973.
4. P. Embrechts and A. M. Herzberg, "Variations of Andrews' plots," Int. Statist. Rev., vol. 59, no. 2, pp. 175-194, 1991.
5. R. W. Hamming, Numerical Methods for Scientists and Engineers, 2nd ed. New York: Dover, 1973.
6. Handbook of Mathematics, Van Nostrand Reinhold, New York, 1985.
7. M. L. Boas, Mathematical Methods in the Physical Sciences. New York: Wiley, 1966.
8. I. Daubechies, "Ten Lectures on Wavelets," Society for Industrial and Applied Mathematics, 1992.
9. A. N. Shiryaev, Probability. New York: Springer-Verlag, 1996.
10. B. Noble and J. W. Daniel, Applied Linear Algebra. Englewood Cliffs, NJ: Prentice-Hall, 1977.
11. M. H. Hassoun, Fundamentals of Artificial Neural Networks Cambridge, MA: MIT Press, 1995.
12. A. Basilevsky, Applied Matrix Algebra in the Statistical Sciences. New York: North-Holland, 1983.
13. F. Moss and K. Wiesenfeld, "The benefits of background noise," Sci. Amer., Aug. 1995.
14. L. Gammaitoni, P. Hanggi, P. Jung, and F. Marchesoni, "Stochastic resonance," Rev. Modern Phys., vol. 70, no. 1, pp. 223-287, 1998.