Guha Lab

Bhaswar Chakrabarti

  • Postdoctoral Researcher

Bhaswar Chakrabarti is a postdoctoral scholar at the University of Chicago's Pritzker School of Molecular Engineering. He is broadly interested in the areas of electronic devices, oxide based high-k dielectric materials for non-volatile memories, and neuromorphic computation. His current research is aimed at the investigation of novel material systems for the development of low power artificial synapses. Bhaswar has previously worked as a post-doctoral scholar (2014-2016) at the University of California, Santa Barbara, where he was involved in the development of neuromorphic circuits with memristive devices. He was responsible for the first demonstration of a monolithic 3D CMOS-memristor hybrid integrated circuit with the capability to work as a high performance multiply-add engine. His other works at UCSB include development of a multi-layer deep neural network (perceptron) with memristive crossbar arrays.

Bhaswar received his BTech in radiophysics and electronics from the University of Calcutta, Kolkata, India, in 2005 and MTech in nanoscience and technology from Jadavpur University, Kolkata, India, in 2007. He obtained his PhD in materials science and engineering from the University of Texas at Dallas, Richardson, in 2013. His doctoral research primarily focused on the development of low power forming-free resistive switching memory devices using high-k dielectrics and novel two-dimensional electrode materials. He has authored/co-authored more than 20 journal publications and international conference proceedings including publications in Scientific Reports, Nature Communications, Nanoscale, IEEE Electron Device Letters, Applied Physics Letters, and IEEE Electron Devices Meeting. He has also authored a book chapter on neural networks with memristive crossbars and has a pending US patent on the development of an ultra-low power non-volatile resistive switching memory.

Dr. Chakrabarti's current research is aimed at the development of low power artificial synapses for neuromorphic circuits. 

Predictive framework for electrode selection enables silicon compatible Sn-based resistive switching

S. Sonde, B.Chakrabarti, Y. Liu, K. Sasikumar, J. Lin, L. Stan, R. Divan, L. E. Ocola, D. Rosenmann, P. Choudhury, K. Ni, S. Sankaranarayanan, S. Datta and S. Guha. Predictive framework for electrode selection enables silicon compatible Sn-based resistive switching. Nanoscale. 2018. Vol. 10, Pg. 9441-9449.