Work

A Hybrid Polynomial Chaos Expansion and Gaussian Process Regression Method for Forward Uncertainty Quantification of Integrated Circuits

Year: 2024

Type: conference-paper

Abstract: This paper introduces a novel kernel-based formulation for the efficient uncertainty quantification of integrated circuits. The method combines the polynomial chaos expansion (PCE) and Gaussian process regression (GPR) frameworks, the former to provide closed-form statistical information and the latter for an efficient training. In essence, the PCE coefficients are computed using a suitable Bayesian formulation that involves the definition of a special implicit kernel based on an infinite sequence of Hermite polynomials. The proposed method is illustrated based on a network with microstrip lines.

Source: IEEE Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)

DOI: 10.1109/epeps61853.2024.10754236

Authors: Paolo Manfredi, Riccardo Trinchero

Institution: Politecnico di Torino

Language: English

Topic: Probabilistic and Robust Engineering Design

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W4404577386