Computational intelligence characterization method of semiconductor device

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. We propose a novel multiple trip point characterization concept to overcome the constraint of the single trip point concept in the device characterization phase. In addition, we use computational intelligence techniques (e.g., neural networks, fuzzy and genetic algorithms) to manipulate further these sets of multiple trip point values and tests based on semiconductor test equipment. Our experimental results demonstrate an excellent design parameter variation analysis in the device characterization phase, as well as detection of a set of worst case tests that can provoke the worst case variation, while the traditional approach was not capable of detecting them.