Optimizing semiconductor devices by self-organizing particle swarm

TL;DR

This paper introduces a self-organizing particle swarm optimization method that adapts through particle replacement and fluctuation mechanisms, leading to faster convergence and improved device optimization performance.

Contribution

It presents a novel self-organizing particle swarm algorithm with adaptive particle replacement and fluctuation strategies for enhanced optimization in semiconductor devices.

Findings

Faster convergence in simplified models.

Effective performance improvement on benchmark functions.

Successful application to device optimization.

Abstract

A self-organizing particle swarm is presented. It works in dissipative state by employing the small inertia weight, according to experimental analysis on a simplified model, which with fast convergence. Then by recognizing and replacing inactive particles according to the process deviation information of device parameters, the fluctuation is introduced so as to driving the irreversible evolution process with better fitness. The testing on benchmark functions and an application example for device optimization with designed fitness function indicates it improves the performance effectively.