Satyaprasad Senanayak
NISER, Bhubaneswar
Satyaprasad P Senanayak is Reader in Physics who heads the Nanoelectronics and Device Physics Lab at the National Institute of Science Education at Research (NISER), Bhubaneswar. He received his Ph.D. in Physics from JNCASR, Bangalore (2009 – 2015) for his work on developing high performance, low power, fast switching polymer field effect transistors. He then pursued his postdoctoral research at the Cavendish Laboratory, University of Cambridge working on developing Perovskite Field effect transistors. He is awarded the prestigious Royal Society Newton Fellowship, EPSRC Global Challenge Award, Royal Society Alumni Fellowship, Indo-US Science and Technology Forum Fellowship and Young Scientist Award from the Odisha Bigyan Academy. He is an Early career member of American Physical Society and Associate of Indian Academy of Sciences. His current research at NISER addresses three focused areas: (a) Development of high performance perovskite optoelectronic devices; (b) Thermoelectric Devices; (c) Neuromorphic Computing.
Session 2A: Lectures by Fellows/Associates
Chairperson: Sanjay Kumar, BHU, Varanasi
Polarization engineering for artificial neural circuits
Neuromorphic computing architectures that emulate brain-like information processing offer transformative potential for developing energy-efficient artificial intelligence and next-generation in-memory computing systems. In this talk, we present two complementary strategies for realizing neuromorphic device functionalities using solution-processed organic and hybrid materials. The first approach leverages ionotronic mechanisms, where mobile ions within the active layer dynamically modulate the channel conductance, thereby mimicking short-term and long-term synaptic plasticity through ion migration and accumulation. The second strategy exploits ferroelectric polarization switching, in which the reversible alignment of electric dipoles induces nonvolatile modulation of charge carrier density, leading to stable bi-stable states suitable for long-term memory and programmable synaptic weights. The devices exhibit robust memory retention and endurance, enabling effective handwritten pattern recognition. Overall, this work establishes a materialsand mechanism-driven framework for developing energy-efficient, non–von Neumann computing architectures that can bridge the gap between artificial and biological intelligence.