Before joining MERL as a visiting member research staff, Chungwei was a postdoctoral researcher in the Physics Department of the University of Texas at Austin. He has worked on transition metal oxides including manganites and titanates. His particular interest is the use of doping/interface to control optical, thermal, and transport properties. In addition to oxides, he has worked on the theory of self-assembly, configuration interaction quantum impurity solvers, and the theory of photoemission spectroscopy.
Where: Physical Chemistry Chemical Physics – Published 22 Feb 2019
MERL Contact: Chungwei Lin
Research Areas: Applied Physics, Multi-Physical ModelingBrief
Date: June 12, 2019
- The journal "Physical Chemistry Chemical Physics (PCCP)" selects a few well-received articles highlighted as HOT by the handling editor or referees. The following paper "Band Alignment in Quantum Wells from Automatically Tuned DFT+U" with MERL authors Grigory Kolesov, Chungwei Lin, Andrew Knyazev, Keisuke Kojima, Joseph Katz has been selected as a 2019 HOT Physical Chemistry Chemical Physics article, and is made free to access until the end of July 2019. This paper provides a semi-empirical methodology to compute the lattice and electronic structures of systems composed of 400+ atoms. The efficiency of this method allows for realistic simulations of interfaces between semiconductors, which is nearly impossible using the existing methods due to the extremely large degrees of freedom involved. The formalism is tested against a few established band alignments and then applied to determine the band gaps of quantum wells; the agreement is within the experimental uncertainty.
Where: Scientific Reports, open-access journal from Nature Research
MERL Contacts: Devesh Jha; Toshiaki Koike-Akino; Keisuke Kojima; Chungwei Lin; Kieran Parsons; Bingnan Wang
Research Areas: Artificial Intelligence, Electronic and Photonic Devices, Machine Learning, CommunicationsBrief
Date: February 4, 2019
- MERL researchers developed a novel design method enhanced by modern deep learning techniques for optimizing photonic integrated circuits (PIC). The developed technique employs residual deep neural networks (DNNs) to understand physics underlaying complicated lightwave propagations through nano-structured photonic devices. It was demonstrated that the trained DNN achieves excellent prediction to design power splitting nanostructures having various target power ratios. The work was published in Scientific Reports, which is an online open access journal from Nature Research, having high-impact articles in the research community.
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- "Quantification of Rolling-Element Bearing Fault Severity of Induction Machines", International Electric Machine & Drives Conference (IEMDC), May 2019. ,
- "Nanostructured Photonic Power Splitter Design via Convolutional Neural Networks", Conference on Lasers and Electro-Optics (CLEO), May 2019. ,
- "Band Alignment in Quantum Wells from Automatically Tuned DFT+U", Physical Chemistry Chemical Physics, No. 11, March 2019. ,
- "Bean's Critical-State Model as a Consequence of the Circuit Model of Non-linear Resistance", Journal of Applied Physics, DOI: 10.1063/1.5084152, Vol. 125, No. 9, March 2019. ,
- "Deep Neural Network Inverse Modeling for Integrated Photonics", Optical Fiber Communication Conference and Exposition and the National Fiber Optic Engineers Conference (OFC/NFOEC), DOI: 10.1364/OFC.2019.W3B.5, March 2019. ,
Title: "Semiconductor Device with Multi-Function P-Type Diamond Gate"
Inventors: Teo, Koon Hoo; Tang, Chenjie; Lin, Chungwei
Patent No.: 9,780,181
Issue Date: Oct 3, 2017
- Title: "Semiconductor Device with Multi-Function P-Type Diamond Gate"