Abstract

PCB/package stack-up design optimization is time-consuming and requiring a great deal of experience. Although some iterative optimization algorithms are applied to implement automatic stack-up design, evaluating the results of each iteration is still time-intensive. This paper proposes a combined Bayesian optimization-artificial neural network (BO-ANN) algorithm, utilizing a trained ANN-based surrogate model to replace a 2D cross-section analysis tool for fast PCB/package stack-up design optimization. With the acceleration of ANN, the proposed BO-ANN algorithm can finish 100 iterations in 40 seconds while achieving the target characteristic impedance. To better generalize the BO-ANN algorithm, a strategy of effective dielectric calculation is applied to multiple-dielectric stack-up optimization. the BO-ANN algorithm will be able to output optimized stack-up designs with dielectric layers chosen from the pre-defined library and the obtained designs are verified by 2D solver.

Department(s)

Electrical and Computer Engineering

Comments

National Science Foundation, Grant IIP-1916535

Keywords and Phrases

artificial neural network; Bayesian optimization; deep learning; PCB/package stack-up design

International Standard Serial Number (ISSN)

0569-5503

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2023 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2023

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