Sayani Majumdar

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Associate Professor

University of Tampere

Finland

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About

Dr. Sayani Majumdar is an Associate Professor at Tampere University, Finland. Her research focuses on advanced materials and devices for neuromorphic computing, memristive systems, and ferroelectric memory applications. Recent work includes exploring fault-tolerant sensor fusion in autonomous vehicles and developing compact models for ferroelectric capacitors at cryogenic temperatures. She is committed to advancing the field of analog memory and deep learning accelerators through innovative material synthesis and device design.

Recent Grants

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Complex Oxide Interfaces for Nanoelectronics

Open Date: 2015-09-01

Close Date: 2020-08-01

Articles (15)

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2025

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2020

Collaborators (5)

Mirosław Sawczak

Institute of Fluid Flow Machinery

POLAND

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Rafał Jendrzejewski

Institute of Fluid Flow Machinery

POLAND

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Ishan Pande

Aalto University

FINLAND

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Hongwei Tan

Aalto University

FINLAND

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Minnamari Saloaro

University Lecturer

University of Turku

FINLAND

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Sayani Majumdar

Research Interests

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Contact this professor

About

Recent Grants

Articles (15)

A Memristive Associative Learning Circuit for Fault-Tolerant Multi-Sensor Fusion in Autonomous Vehicles

Can ferroelectric tunnel junction be a game changer as eNVM and in neuromorphic hardware?

A Physics‐Based Compact Model for Ferroelectric Capacitors Operating Down to Deep Cryogenic Temperatures for Applications in Analog Memory and Neuromorphic Architectures

Understanding fatigue and recovery mechanisms in Hf0.5Zr0.5O2 capacitors for designing high endurance ferroelectric memory and neuromorphic hardware

Ferroelectric Hf0.5Zr0.5O2 for Analog Memory and In‐Memory Computing Applications Down to Deep Cryogenic Temperatures

Harnessing ferroic ordering in thin film devices for analog memory and neuromorphic computing applications down to deep cryogenic temperatures

Back‐End and Flexible Substrate Compatible Analog Ferroelectric Field‐Effect Transistors for Accurate Online Training in Deep Neural Network Accelerators

Large-area synthesis of high electrical performance MoS2 by a commercially scalable atomic layer deposition process

Giant magnetoresistance response in Sr<sub>2</sub>FeMoO<sub>6</sub> based organic spin valves

An efficient deep neural network accelerator using controlled ferroelectric domain dynamics

Quantum Conductance in Memristive Devices: Fundamentals, Developments, and Applications

Back‐End CMOS Compatible and Flexible Ferroelectric Memories for Neuromorphic Computing and Adaptive Sensing

Rubrene Thin Films with Viably Enhanced Charge Transport Fabricated by Cryo-Matrix-Assisted Laser Evaporation

Ultrafast switching and linear conductance modulation in ferroelectric tunnel junctions via P(VDF-TrFE) morphology control

Tactile sensory coding and learning with bio-inspired optoelectronic spiking afferent nerves

Collaborators (5)

Mirosław Sawczak

Rafał Jendrzejewski

Ishan Pande

Hongwei Tan

Minnamari Saloaro

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