Sayani Majumdar

My core research interest is development of Neuromorphic Computing hardware for next generation of energy-efficient, intelligent and adaptive computing and sensing. More specifically, my team's research focus is Non-volatile Memories, synaptic weight elements and leaky-integrate and fire neuron components based on Back-end CMOS compatible Ferroelectric thin films and oxide or 2D semiconductor based heterostructures. My research experience and interest also includes Spintronic components like magnetic tunnel junctions and different other emerging solid state electronic devices including optoelectronic and flexible and wearable sensor components.

Teaching Courses on Semiconductor Device Physics (spring 2024) and Future Computing Devices and Architecture (upcoming, Fall 2024) and Research on Beyond CMOS computing devices and systems.

Micro and Nanoscale Solid-state Electronic Device design and fabrication; Characterization; Modelling; and Neuromorphic Computing.

Broadly, my team's research goal is Development of Neuromorphic Computing hardware based on low thermal budget and sustainable materials and processes. To achieve this goal, we work on different sub-topics ranging from materials, devices, modelling, design, high-end fabrication and system level integration.

• Neuromorphic Computing and Adaptive Sensing for Extreme Edge devices
• Low-thermal budget ferroelectric memories
• Atomic Layer Deposited (ALD) thin film devices

Electrical Engineering

Neuromorphic Computing hardware; Non-volatile memories; Sensing; Optoelectronics, Cryogenic Electronics for Quantum Computing

Research Council of Finland

• 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, I initiated and led research on hybrid spintronic components. 
• I was chosen a Junior Group Leader by Turku Collegium for Science and Medicine working on Spintronic Components between 2010-2013.
• 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 my key research 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 (FeFET, FeTFT) 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).
• Ultrafast Switching and Linear Conductance Modulation in Ferroelectric Tunnel Junctions via P (VDF-TrFE) Morphology Control, S Majumdar, Nanoscale 13, 11270-11278 (2021).
• 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).
• Crossover from synaptic to neuronal functionalities through carrier concentration control in Nb-doped SrTiO3-based organic ferroelectric tunnel junctions, S Majumdar, H Tan, I Pande, S van Dijken, APL Materials 7, 091114 (2019).
• 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).