2023/08/18 by Ping-Ju Chuang, Ali Saadat, Chuang, Ping-Ju +7
Computer Science · Engineering · #Advanced Multi-Objective Optimization Algorithms #Advancements in Semiconductor Devices and Circuit Design #FOS: Computer and information sciences #FOS: Electrical engineering #Low-power high-performance VLSI design #Machine Learning (cs.LG) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2308.09612
openalex publication_date 2023/08/18 · openalex created_date 2023/08/22 · openalex updated_date 2026/07/28
We propose a novel constrained Bayesian Optimization (BO) algorithm optimizing the design process of Laterally-Diffused Metal-Oxide-Semiconductor (LDMOS) transistors while realizing a target Breakdown Voltage (BV). We convert the constrained BO problem into a conventional BO problem using a Lagrange multiplier. Instead of directly optimizing the traditional Figure-of-Merit (FOM), we set the Lagrangian as the objective function of BO. This adaptive objective function with a changeable Lagrange multiplier can address constrained BO problems which have constraints that require costly evaluations, without the need for additional surrogate models to approximate constraints. Our algorithm enables a device designer to set the target BV in the design space, and obtain a device that satisfies the optimized FOM and the target BV constraint automatically. Utilizing this algorithm, we have also explored the physical limits of the FOM for our devices in 30 - 50 V range within the defined design space.