Computational Intelligence Characterization Method of Semiconductor Device

TL;DR

This paper introduces a novel multiple trip point characterization method for semiconductor devices, utilizing computational intelligence techniques to improve detection of design weaknesses and worst-case variations beyond traditional single trip point approaches.

Contribution

It proposes a new multiple trip point concept combined with neural networks, fuzzy logic, and genetic algorithms for enhanced semiconductor device characterization.

Findings

Effective detection of worst-case tests and design parameter variations.

Superior analysis of device weaknesses compared to traditional methods.

Demonstrated success with experimental results on semiconductor test data.

Abstract

Characterization of semiconductor devices is used to gather as much data about the device as possible to determine weaknesses in design or trends in the manufacturing process. In this paper, we propose a novel multiple trip point characterization concept to overcome the constraint of single trip point concept in device characterization phase. In addition, we use computational intelligence techniques (e.g. neural network, fuzzy and genetic algorithm) to further manipulate these sets of multiple trip point values and tests based on semiconductor test equipments, Our experimental results demonstrate an excellent design parameter variation analysis in device characterization phase, as well as detection of a set of worst case tests that can provoke the worst case variation, while traditional approach was not capable of detecting them.