Zhenhan Huang

Zhenhan Huang


I implemented the data from a significant semiconductor fabrication company. Through supervised machine learning, I build a Random Forest classifier with up to 96% accuracy to detect defective wafers/lots after they have been produced, and I study which particular signals indicate the most to faults in fabricating. This research can provide information and support to prevent future failures in semiconductor fabrication.

Submitted on 2019-10-11

We work on optimizing TSA security inspection process to improve the function of the airport security system and to help reduce passengers’ total time costs to get to the gates from security checkpoints. Our evaluation of a design of TSA security checkpoint process involves two aspects: Effectiveness and Cost. Based on this double-criterion evaluation process, we build an effectiveness-versus-cost strategy matrix in our conclusion to satisfy advisees with different demands and budgets. The major part of this paper will be focusing on discussing how we model and simulate the original process and all other 5 new versions with the proposed modification implemented. Our programmed models are constructed from the ground up, based on maximized realism. We verify the validity of the original version of the model by comparing the bottlenecks it identifies with our real- life experience. Then we formulate 5 new designs that target the bottlenecks to resolve the problem of long queueing time and high variance. The most insightful section of our paper is data Analysis. Data analysis is vitally crucial to the construction of our models. One important achievement of us is that we discovered the tetra-modal pattern of the ”time to get scanned property” data and interpret it in an inspirational way to excavate a huge amount of important information.

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