Abstract: The quick development of AI and machine learning necessitates high-speed, energy-efficient hardware. Data switching between the CPU and memory causes energy inefficiencies in traditional von Neumann designs. Bio-inspired methods, such as neuromorphic and in-memory computing, are being investigated to overcome this. Memristors, which resemble biological synapses, neurons, and dendrites present a viable remedy by fusing brain-like capabilities with logic and data storage functions.

Our objective is to create electronic modules and low-voltage memristors for next-generation computing. Memristors, two-terminal nano-electronic devices, offer high performance, low power consumption, and compact size, making them ideal for next-generation computing and multidimensional integration. In short, this thesis describes the approaches to achieving a high-performing device with the integration of 2D materials and controlling the device performance. This thesis describes how to use chemical vapor deposition (CVD) to create centimeter-scale, large-area MoS2 with regulated thickness by adjusting important growth factors. In the second section, the process of creating defects in 2D samples was described, along with how external parameters, such as defect formation, might alter device performance. Thirdly, the integration of non-defective large-scale 2D material into a neuromorphic device can result in a highly useful application, such as reservoir computing.

Event Details
Title: Investigation of Resistive switching in Two Dimensional (2D) Transition Metal Dichalcogenide (TMDC) Films for Brain Inspired Computation (PhD Viva Voce)
Date: October 14, 2026 at 05:00 PM
Venue: Google Meet (https://meet.google.com/kmz-fswf-hgj)
Speaker: Ms. Thool Asmita Sanjayrao (EE19D011)
Guide: Dr. Bhaswar Chakrabarti
Type: PHD seminar

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