A Methodological Approach for Detecting Burst Noise in the Time Domain

Authors: Liu Dan, Wang Xue, Wang Guiqin, Qian Zhihong

Abstract:

The burst noise is a kind of noises that are destructive and frequently found in semiconductor devices and ICs, yet detecting and removing the noise has proved challenging for IC designers or users. According to the properties of burst noise, a methodological approach is presented (proposed) in the paper, by which the burst noise can be analysed and detected in time domain. In this paper, principles and properties of burst noise are expounded first, Afterwards, feasibility (viable) of burst noise detection by means of wavelet transform in the time domain is corroborated in the paper, and the multi-resolution characters of Gaussian noise, burst noise and blurred burst noise are discussed in details by computer emulation. Furthermore, the practical method to decide parameters of wavelet transform is acquired through a great deal of experiment and data statistics. The methodology may yield an expectation in a wide variety of applications.

Keywords: Burst noise, detection, wavelet transform

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1331793

References:

[1] Yasuo Ebara, Hideaki Sone and Yoshiaki Nemoto. Correlation between arcing phenomena and electromagnetic noise of opening electric contacts. Proceedings of the Forty-Sixth IEEE Holm Conference on 25-27 Sept. 2000 Page(s):191 - 197.
[2] S T Hsu, R J Whitter, C A Mead. Physical model for burst noise in semiconductor devices. Solid-state Electronics,1970,13:1055-1071.
[3] Dai Yisong. Electronics in Noise. Shang Dong: Shang Dong Science&Technology Press, 1997..3..
[4] R. Neelamani, Choi Hyeokho, R. Baraniuk. ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems. IEEE Transactions on Signal Processing, Feb. 2004, 52 (2): 418 - 433.
[5] Yang Zongkai. Noise Elimination by Wavelet and Its Application to Signal Detection. Journal of Huazhong University of Science and Technology, Feb., 1997, 25(2):1-4.
[6] A. Tarczynski, N. Allay. Spectral analysis of randomly sampled signals: suppression of aliasing and sampler jitter. IEEE Transactions on Signal Processing, Dec. 2004, 52 (12): 3324 - 3334.
[7] Dakai Wang, Jinye Wang. Wavelet analysis and the application of signal processing. Beijing: Publishing House of Electronic Industry, 2006.
[8] Rao A. and Jones D. Nonstationary array signal detection using time-frequency and time -scale representations. Proc. ICASSP 1998: 1989-1992.
[9] Colonnese S. and Scarano G. Transient signal detection using higher order moments. IEEE Trans. on SP, 1999, 45(2): 515-521.
[10] Kulkarni S., et. al. Nonuniform M-Band wavepackets for transient signal detection. IEEE Trans. on SP, 2000, 48(6): 1803-1807.
[11] Chen Xiaojuan Zhao Rui. Detection of Burst Noise Based on Wavelet. Proceedings of the Second International Symposium on Test Automation & Instrumentation(Vol.3), 2008.
[12] S. Mallat, and Liang Hwang W. Singularity detection and processing with wavelets. IEEE Trans. IT., 1992, 38(2):617-643..
[13] A. Grossmann and J. Morlet. Decomposition of hardy functions into square integrable wavelets of constant shape. SIAM J. Math. Anal., Jul. 1984, 15(4): 723-736.
[14] L. Hwang, S. Mallat. Singularities and noise discrimination with wavelets. Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference, 23-26 March 1992, 4: 377 - 380.
[15] K. Usman, , H. Juzoji, , I. Nakajima, M.A. Sadiq. A study of increasing the speed of the independent component analysis (IA) using wavelet technique. Proceedings. 6th International Workshop on Enterprise Networking and Computing in Healthcare Industry, HEALTHCOM 2004, 28-29 June 2004: 73 - 75.
[16] Chen Fengshi. The Wavelet Transform Theory and Its Applications in Signal Processing. Beijing: National Defense Industry Press, 1998.
[17] Dai Yisong. Investigation of G-R Noise Induced by Defects in P-N Junetion of Bipolar Transistor. Chinese Journal of Semiconductors,1989,10(1) : 47-54.
[18] David L. Donoho. De-noising by soft-thresholding. IEEE Transactions on Information Theory, May 1995, 41(3): 613-627.
[19] Pantelis D. Agoris, Sander Meijer, Edward Gulski, Johan J. Smit. Threshold selection for wavelet de-noising of partial discharge data. Conference Record of the 2004 EEE International Symposium on Electrical Insulation, Indianapolis, USA, Sep. 19-22, 2004, 62-65.
[20] Agoris, P.D. Meijer, S. Gulski, E. Smit, J.J. Threshold selection for wavelet denoising of partial discharge data. Conference Record of the 2004 IEEE International Symposium on E lectrical Insulation, 19-22 Sept. 2004: 62- 65.