A distributed simulation-based computational intelligence algorithm for nanoscale semiconductor device inverse problem

In this paper, a distributed simulation-based computational intelligence algorithm for inverse problem of nanoscale semiconductor device is presented. This approach features a simulation-based optimization strategy, and mainly integrates the semiconductor process simulation, semiconductor device simulation, evolutionary strategy, and empirical knowledge on a distributed computing environment. For a set of given target current-voltage (I-V) curves of metal-oxide-semiconductor field effect transistors (MOSFETs) devices, the developed prototype executes evolutionary tasks to solve an inverse doping profile problem, and therefore optimize fabrication recipes. In the evolutionary loop, the established management server allocates the jobs of process simulation and device simulation on a PC-based Linux cluster with message passing interface (MPI) libraries. Good benchmark results including the speed-up, the load balancing, and the parallel efficiency are presented. Computed results, compared with the realistic measured data of 65 nm n-type MOSFET, show the accuracy and robustness of the method.