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Thermal transport property of homogeneous twisted molybdenum disulfide (MoS$_2$) is investigated using non-equilibrium molecular dynamics simulations with the state-of-art force fields. The simulation results demonstrate that the…
The present work experimentally demonstrates the fabrication of CVD grown monolayer MoS2 ultra thin quantum well based double barrier resonant tunneling device (RTD) architecture well compatible with conventional CMOS fabrication…
Defects can strongly influence the electronic, optical and mechanical properties of 2D materials, making defect stability under different thermodynamic conditions crucial for material-property engineering. In this paper, we present an…
We present a combined experimental and computational study of two-dimensional molybdenum disulfde (MoS2) and the effect of temperature on the frequency shifts of the Raman-active E2g and A1g modes in the monolayer. While both peaks show an…
Nanomagnetic logic, in which the outcome of a computation is embedded into the energy hierarchy of magnetostatically coupled nanomagnets, offers an attractive pathway to implement in-memory computation. This computational paradigm avoids…
Neuromorphic functionalities in memristive devices are commonly associated with the ability to electrically create local conductive pathways by resistive switching. The archetypal correlated material, VO2, has been intensively studied for…
Defects in atomically thin materials can drive new functionalities and expand applications to multifunctional systems that are monolithically integrated. An ability to control formation of defects during the synthesis process is an…
Abstract: Bionic learning with fused sensing, memory and processing functions outperforms artificial neural networks running on silicon chips in terms of efficiency and footprint. However, digital hardware implementation of bionic learning…
We address an inverse problem in modeling holographic superconductors. We focus our research on the critical temperature behavior depicted by experiments. We use a physics-informed neural network method to find a mass function $M(F^2)$,…
Information technologies require entangling data stability with encryption for a next generation of secure data storage. Current magnetic memories, ranging from low-density stripes up to high-density hard drives, can ultimately be detected…
Magnons, bosonic quasiparticles carrying angular momentum, can flow through insulators for information transmission with minimal power dissipation. However, it remains challenging to develop a magnon-based logic due to the lack of efficient…
The nonvolatile magnetoresistive random access memory (MRAM) is believed to facilitate emerging applications, such as in memory computing, neuromorphic computing and stochastic computing. Two dimensional (2D) materials and their van der…
Spintronics has gone through substantial progress due to its applications in energy-efficient memory, logic and unconventional computing paradigms. Multilayer ferromagnetic thin films are extensively studied for understanding the domain…
We study the transport properties of deeply scaled monolayer MoS2 n-channel metal-oxide-semiconductor field effect transistors (MOSFETs) using full-band ballistic quantum transport simulations with an atomistic tight-binding Hamiltonian…
Memristors based on two-dimensional materials (2DMs) have garnered significant attention due to their fast resistive switching (RS) behavior and atomic-level thickness, which enables low power consumption, making them promising candidates…
A finite Schottky barrier and large contact resistance between monolayer MoS2 and electrodes are the major bottlenecks in developing high-performance field-effect transistors (FETs) that hinder the study of intrinsic quantum behaviors such…
Self-heating in next-generation, high-power-density field-effect transistor limits performance and complicates fabrication. Here, we introduce NEP-FET, a machine-learned framework for device-scale heat transport simulations of field-effect…
The thesis investigates the utilization of memristive and memcapacitive crossbar arrays in low-power machine learning accelerators, offering a comprehensive co-design framework for deep neural networks (DNN). The model, implemented through…
Artificial intelligence is widely used in everyday life. However, an insufficient computing efficiency due to the so-called von Neumann bottleneck cannot satisfy the demand for real-time processing of rapidly growing data. Memristive…
Developing electronic devices capable of emulating biological functions is essential for advancing brain-inspired computation paradigms such as neuromorphic computing. In recent years, two-dimensional materials have emerged as promising…