Algorithmic Optimization of Transistors Applied to Silicon LDMOS

We propose a pioneering approach that integrates optimization algorithms and technology computer-aided design to automatically optimize laterally-diffused metal-oxide-semiconductors (LDMOS) with a field-oxide structure. We define the ratio of the square of the breakdown voltage divided by the specific on-resistance as the figure-of-merit (FOM) and the objective function of our optimization. We compare the performance of three different algorithms: Nelder-Mead, Powell, and Bayesian Optimization. We show how the LDMOS performance evolves as each of the three optimization algorithms reach their optimized structure. We show that a straightforward Nelder-Mead optimization leads to a local optimum when optimizing over six input parameters. We find that Bayesian Optimization is the most data-efficient method to find the global optimized structure in the multi-domain design space.