Abstract: The continuous demand for higher performance, lower power consumption, and increased data storage has driven the exploration of emerging non-volatile memory (eNVM) technologies and in-memory computing (IMC) architectures. Among these, hafnium oxide (HfO₂)-based ferroelectric field-effect transistors (FeFETs) have emerged as promising candidates due to their fast-switching speed, low power operation, and CMOS compatibility. Despite these advantages, the practical deployment of FeFET-based computing systems faces significant challenges related to device variability and reliability. Variability primarily originates from the polycrystalline nature of HfO₂ ferroelectric films, leading to drain-current fluctuations that can degrade computational accuracy in neuromorphic and IMC applications. To mitigate this, this work proposes a compact 1FeFET–1T (1F–1T) memory cell in which a cascaded transistor limits the ON-state current. Simulation results demonstrate a substantial reduction in bit-line current variation, leading to improved vector–matrix multiplication accuracy.

In addition to variability, endurance degradation remains a major reliability bottleneck in FeFETs. Using the charge pumping technique, this work investigates gate stack degradation induced by cycling and identifies interface trap generation as the dominant failure mechanism. Based on these insights, optimized program/erase pulse schemes are proposed to enhance endurance. Finally, this work explores FeFET-based in-memory computing applications beyond conventional memory. A compact 1FeFET-1T content-addressable memory (CAM) cell is demonstrated for parallel search operation. Furthermore, spike-timing-dependent plasticity (STDP) is experimentally demonstrated using a single FeFET device, highlighting the potential of FeFETs for event-driven computing.

Event Details
Title: Ferroelectric Field Effect Transistors: Design, Characterization and Applications (PhD Viva Voce)
Date: January 12, 2026 at 11:00 AM
Venue: ESB-244 (Seminar Hall)/ Google Meet
Speaker: Mr. Masud Rana SK (EE20D405)
Guide: Dr. Bhaswar Chakrabarti
Type: PHD seminar

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