Sayani Majumdar

My core research interest is development of Neuromorphic Computing hardware for next generation of energy-efficient and adaptive computing and sensing. More specifically, I work on Non-volatile Memories based on Ferroelectric thin film and oxide and 2D semiconductor devices. My research experience and interest also includes Spintronic devices and different other emerging solid state electronic devices including optoelectronic and sensor components.

Electrical Engineering

• I did my Ph. D. from Indian Association for the Cultivation of Science in 2006 working on fundamental physics of complex oxide materials down to deep cryogenic temperatures. 
• As a post-doctoral fellow in Åbo Akademi University and University of Turku in Finland, my research interest diversified to application of thin film oxide materials in hybrid spintronic components. 
• I worked as a visiting scientist in Francis Bitter Magnet Laboratory, Massachusetts Institute of Technology, USA in 2010 and 2011, working on magnetic tunnel junctions. 
• I was an Academy Research Fellow in Aalto University, Finland , with key focus on ferroelectric tunnel junction based memristors and their application as electronic synapses and neurons in neuromorphic computing. 
• As a senior scientist in VTT, my research portfolio was further diversified to include development of 2D semiconductor based memory and logic components based on ALD grown Hafnia based ferroelectric memory devices for energy-efficient hardware for neuromorphic computing.

• Modelling Ferroelectric Hysteresis of HZO Capacitor with Jiles-Atherton Model for Non-Volatile Memory Applications, Ella Paasio, Mika Prunnila, Sayani Majumdar, IEEE 12th Non-Volatile Memory Systems and Applications Symposium (NVMSA), Niigata, Japan, pp.1, 2023. doi: 10.1109/NVMSA58981.2023.00019.
• Back-end and Flexible Substrate Compatible Analog Ferroelectric Field Effect Transistors for Accurate Online Training in Deep Neural Network Accelerators, S Majumdar, I Zeimpekis, Advanced Intelligent Systems, 2300391 (2023).
• Large-area synthesis of high electrical performance MoS2 by a commercially scalable atomic layer deposition process, N Aspiotis, K Morgan, B März, K Müller-Caspary, M Ebert, E Weatherby, S Majumdar, I Zeimpekis, npj 2D Materials and Applications 7 (1), 18 (2023).
• An efficient deep neural network accelerator using controlled ferroelectric domain dynamics, S Majumdar, Neuromorphic Computing and Engineering 2 (4), 041001 (2022).
• Back-End CMOS Compatible and Flexible Ferroelectric Memories for Neuromorphic Computing and Adaptive Sensing, S Majumdar, Advanced Intelligent Systems 4, 2100175 (2022).
• Tactile sensory coding and learning with bio-inspired optoelectronic spiking afferent nerves, H Tan, Q Tao, I Pande, S Majumdar, F Liu, Y Zhou, POÅ Persson, J Rosen, S van Dijken, Nature communications 11 (1), 1369 (2020).
• Energy‐Efficient Organic Ferroelectric Tunnel Junction Memristors for Neuromorphic Computing, S Majumdar, H Tan, Q Qin, S van Dijken, Advanced Electronic Materials 5, 1800795 (2019).