Fig. 1.
(Color online) The original function and MRR fitting function. (a) Original artificial function. (b) Single-pole MRR fitting result. (c) Step NMSEs of single-pole MRR. (d) Step NMSEs of double-pole MRR.
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Yuxi Hong, Dongsheng Ma, Zuochang Ye. Multivariate rational regression and its application in semiconductor device modeling[J]. Journal of Semiconductors, 2018, 39(9): 094010. doi: 10.1088/1674-4926/39/9/094010
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Y X Hong, D S Ma, Z C Ye, Multivariate rational regression and its application in semiconductor device modeling[J]. J. Semicond., 2018, 39(9): 094010. doi: 10.1088/1674-4926/39/9/094010.
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Multivariate rational regression and its application in semiconductor device modeling
doi: 10.1088/1674-4926/39/9/094010
More Information-
Abstract
Physics equation-based semiconductor device modeling is accurate but time and money consuming. The need for studying new material and devices is increasing so that there has to be an efficient and accurate device modeling method. In this paper, two methods based on multivariate rational regression (MRR) for device modeling are proposed. They are single-pole MRR and double-pole MRR. The two MRR methods are proved to be powerful in nonlinear curve fitting and have good numerical stability. Two methods are compared with OLS and LASSO by fitting the SMIC 40 nm MOS-FET I–V characteristic curve and the normalized mean square error of Single-pole MRR is$3.02 \times {10^{{\rm{ - }}8}}$ which is 4 magnitudes less than an ordinary least square. The I–V characteristics of CNT-FET and performance indicators (noise factor, gain, power) of a low noise amplifier are also modeled by using MRR methods. The results show MRR methods are very powerful methods for semiconductor device modeling and have a strong nonlinear curve fitting ability. -
References
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