Uncertainty Quantification of CMOS Active Filter Circuits: A Non-Intrusive Computational Approach Based on Generalized Polynomial Chaos

• Main Authors: Mecit Emre Duman, Onder Suvak
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Active CMOS filter; generalized polynomial chaos; magnitude response; Monte Carlo methods; stochastic collocation; stokhos
• Online Access: https://ieeexplore.ieee.org/document/9223680/

Semiconductor fabrication technologies as applies to the nanometer-era paradigms of nowadays have rendered uncertainty quantification analyses through component-level parameters compulsory and indispensable. Frequency responses of CMOS active filters are invariably observed to be affected by probabilistically modelled parameter deviations, and in this article the focus is on the fast and accurate quantification of the uncertainties pervading CMOS active filters in terms of their magnitude frequency responses. Previous work dominantly has preference for the widely recognized non-intrusive Monte Carlo methods, which bring about a disproportionately high computational burden. Also discomfitures are observed to arise due to apparently inadequate ensemble volumes and a limited variety of distribution functions that are chosen to be utilized, along with seemingly insufficient means of resulting data visualization and the lack of accurate probabilistic quantification. Generalized Polynomial Chaos (gPC) based stochastic spectral techniques, which usually offer reduced computational complexity with respect to Monte Carlo, targeting CMOS active filters do not seem to have drawn much attention; the few related publications offer utility in a limited scope of electronic components. In this article, we carry out uncertainty quantification analyses in order to compute partial or approximate stochastic characterizations of the magnitude frequency responses of several multi-component CMOS active filter circuits with the gPC-based stochastic collocation technique. The pertaining inherent non-intrusive nature is exploited, and the stated issues associated with the previous work are addressed. We utilize a stokhos-based MATLAB/C++ toolbox of our own design, on whose software architecture, features, and facilities we provide profound details, and present performance comparisons with Monte Carlo along with intuitive and insightful comments, in an endeavor to suggest that such observations may prove to be beneficial to circuit designers.