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.

提出一种新颖的受限贝叶斯优化（BO）算法，用于优化侧向扩散金属氧化物半导体（LDMOS）晶体管的设计过程，实现目标击穿电压（BV）。通过引入拉格朗日乘子，将受限 BO 问题转化为传统 BO 问题，并将拉格朗日函数设置为 BO 的目标函数。利用具有可变拉格朗日乘子的自适应目标函数，可以解决需要耗费昂贵评估的约束 BO 问题，无需额外的替代模型来近似约束条件。该算法使设备设计师能够在设计空间中设置目标 BV，并自动获得满足优化 FOM 和目标 BV 约束的设备。利用该算法，还在定义的设计空间内探索了 30-50V 范围内设备 FOM 的物理限制。