- 131 Stinson-Remick Hall
Wriddhi Chakraborty received his Bachelors’ degree in Electronics and Telecommunication Engineering from Indian Institute of Engineering Science and Technology, Shibpur, India, in 2018. He is currently pursuing Ph.D. degree in Electrical Engineering from University of Notre Dame, Indiana, USA, under Dr. Suman Datta. His current research focuses on cryogenic response of MOSFETs for quantum computing and high-performance computing applications, which involves experimental characterization and device modelling at cryogenic temperature. His research interest also includes investigation of higher-K oxide materials for gate-stack in advanced MOSFET technologies and Ferroelectric Field-effect transistor (FeFET) based neuromorphic hardware. He also received the DAAD-WISE scholarship (2017) for working on a summer research project for developing Quantum-circuit synthesis algorithm at Universitat Bremen, Germany.
1. W. Chakraborty, K. Ni, J. Smith, A. Raychowdhury and S. Datta, “An Empirically Validated Virtual Source FET Model for Deeply Scaled Cool CMOS”, IEEE International Electron Devices Meeting (IEDM), 2019
2. K. Ni, A.K.Saha, W. Chakraborty, B.Grisafe, J. Smith, S.Gupta and S. Datta, “Equivalent Oxide Thickness (EOT) Scaling With Hafnium Zirconium Oxide High-κ Dielectric Near Morphotropic Phase Boundary”, IEEE International Electron Devices Meeting (IEDM), 2019
3. W. Chakraborty, K. Ni, S. Dutta, B. Grisafe, J. Smith and S. Datta,, “Cryogenic Response of HKMG MOSFETs for Quantum Computing Application”, 77th Device Research Conference, 2019
4. S. Dutta, W. Chakraborty, J. Gomez, K. Ni, S. Joshi, S. Datta, “Energy-Efficient Edge Inference on Multi-Channel Streaming Data in 28nm HKMG FeFET Technology”, Symposium on VLSI Technology, 2019
5. K. Ni, W. Chakraborty, J. Smith, B. Grisafe and S. Datta, “Fundamental Understanding and Control of Device-To-Device Variation in Deeply Scaled Ferroelectric FETs”, Symposium on VLSI Technology, 2019
6. W. Chakraborty, R. Ray, N. Samanta, C. RoyChaudhuri, “Quantitative Differentiation of Multiple Virus in Blood using Nanoporous Silicon Oxide Immunosensor and Artificial Neural Network”, Biosensors and Bioelectronics (Impact Factor: 7.780), Elsevier, vol. 98, 15 December 2017.