Arifuzzaman Faisal

Background

About Arifuzzaman Faisal

As a graduate research assistant at Michigan State University (MSU), I am passionate about developing and analyzing theoretical models for semiconductor and vacuum electronic devices, such as FETs, CMOS, PN junctions, photodetectors, TWTs, SWSs, and cavities. I have hands-on experience in various clean room processes, such as thin-film deposition, PECVD, PVD, lithography, etching, RIE, fabrication, and handling hazardous chemicals. I utilize software tools such as CST Studio, HFSS, COMSOL, Sentaurus TCAD, Matlab, and Python for numerical analysis and simulation of electrical properties and electromagnetic phenomena. I hold a master's degree in Electrical and Computer Engineering (ECE) from MSU, and I am currently pursuing a PhD in the same field, expected to graduate in August 2025. I was a visiting PhD student at the Singapore University of Technology and Design from May 2022 to August 2022, where I collaborated with leading researchers in the field of nanoelectronics. I have a strong programming skill set, proficient in C, C++, Python, MATLAB, and parallel computing. I am also skilled in machine learning, including TensorFlow, Pandas, PyTorch, Scikit-learn, segmentation, clustering, supervised-unsupervised ML, regression, SVM, KNN, and deep learning. Summary of Expertise- o Hands-on experience in Clean Room environments. Specialized in processes such as Thin-film Deposition, PECVD, PVD, lithography, etching, RIE, Fabrication, and handling hazardous chemicals. o Proficient in developing and analyzing theoretical models for semiconductor materials and devices (FET, CMOS, PN Junction, Photodetectors), and vacuum electronic devices (TWT, SWS, Cavity). o Utilizes CST Studio, HFSS, COMSOL, Sentaurus TCAD, Matlab, and Python for modeling semiconductor devices to analyze electrical properties and electromagnetic phenomena. o Strong programming skill set: Proficient in C, C++, Python, MATLAB, and Parallel Computing. o Proficient in Machine Learning including TensorFlow, Pandas, PyTorch, Scikit-learn, Segmentation, Clustering, Supervised-Unsupervised ML, Regression, SVM, KNN, and Deep Learning. etc. Summary of Skills- 1. Programming Language: C, C++, Python, MATLAB. Parallel Programming: High-Performance Computing, MPI and OpenMP. Application Software: MATLAB, Simulink, Mathematica, CST Studio, HFSS, COMSOL, Sentaurus TCAD. Machine Learning Framework: Segmentation, Clustering, Supervised-Unsupervised ML, Regression, SVM, KNN, MLP, Random Forest, Deep Learning, TensorFlow, Pandas, PyTorch, Scikit-learn.