TCAD-Based Design of SiGe HBT Germanium Profiles via Bayesian Optimization

This work investigates the automatic design of silicon-germanium (SiGe) heterojunction bipolar transistor (HBT) germanium profiles utilizing Bayesian optimization in a commercial TCAD engine. The time-intensive nature of device simulation renders standard numerical optimization strategies infeasible, as gradients are expensive to approximate, and candidate objective functions, such as device minimum noise figure ($N F_{\text {min }}$), maximum transit frequency $\left(f_{T}\right)$, or breakdown voltage ($B V_{C E O}$) are often not well-behaved functions of design variables. Bayesian optimization is a strategy that seeks a global optimum of an expensive-to-evaluate function of which little underlying structure is assumed. Here, we model the output of TCAD simulations as such a function and show that Bayesian optimization is capable of exploring the Ge profile design space, providing optimized profiles in as few as 30 simulation iterations.