Related papers: A Ferroelectric Tunnel Junction-based Integrate-an…
Superparamagnetic tunnel junctions (sMTJs) are emerging as promising components for stochastic units in neuromorphic computing, owing to their tunable random switching behavior. Conventional MTJ control methods, such as spin-transfer torque…
Multiferroic tunnel junctions (MFTJs) have already been proved to be promising candidates for application in spintronics devices. The coupling between tunnel magnetoresistance (TMR) and tunnel electroresistance (TER) in MFTJs can provide…
This paper proposes a spintronic neuron structure composed of a heterostructure of magnets and a piezoelectric with a magnetic tunnel junction (MTJ). The operation of the device is simulated using SPICE models. Simulation results illustrate…
In tunnel junctions with ferroelectric barriers, switching the polarization direction modifies the electrostatic potential profile and the associated average tunnel barrier height. This results in strong changes of the tunnel transmission…
Magnetic tunnel junctions (MTJs) are the key building blocks of high-performance spintronic devices. While conventional MTJs rely on ferromagnetic (FM) materials, employing antiferromagnetic (AFM) compounds can significantly increase…
Ferroelectric tunnel junction devices based on ferroelectric thin films of solid solutions of hafnium dioxide can enable CMOS integration of ultra-low power ferroelectric devices with potential for memory and emerging computing schemes such…
The rapid growth of brain-inspired computing coupled with the inefficiencies in the CMOS implementations of neuromrphic systems has led to intense exploration of efficient hardware implementations of the functional units of the brain,…
In spiking neural networks, neuron dynamics are described by the biologically realistic integrate-and-fire model that captures membrane potential accumulation and above-threshold firing behaviors. Among the hardware implementations of…
Control of magnetism without using magnetic fields enables large-scale integration of spintronic devices for memory, computation and communication in the beyond-CMOS era. Mechanisms including spin torque transfer, spin Hall effect, and…
Neuromorphic computing promises revolutionary improvements over conventional systems for applications that process unstructured information. To fully realize this potential, neuromorphic systems should exploit the biomimetic behavior of…
Neuromorphic computing uses brain-inspired principles to design circuits that can perform computational tasks with superior power efficiency to conventional computers. Approaches that use traditional electronic devices to create artificial…
Perpendicular magnetic tunnel junctions are one of the building blocks for spintronic memories, which allow fast nonvolatile data access, offering substantial potentials to revolutionize the mainstream computing architecture. However,…
My research is dedicated to the electronic properties of functional oxides. My activity specifically focuses on ferroelectric tunnel junctions in which an ultrathin layer of ferroelectric material is intercalated between two metallic…
Manipulation of tunneling spin-polarized electrons via a ferroelectric interlayer sandwiched between two ferromagnetic electrodes, dubbed Multiferroic Tunnel Junctions (MFTJs), can be achieved not only by the magnetic alignments of two…
Electric-field control of spin states offers a promising route to ultra-low-power, ultra-fast magnetization switching in spintronic devices such as magnetic tunnel junctions (MTJs). Recent progress in modulating spin-orbit interactions at…
Magnetic tunnel junctions (MTJs) are crucial components in high-performance spintronic devices. Traditional MTJs rely on ferromagnetic (FM) materials but significant improvements in speed and packing density could be enabled by exploiting…
We present a design-scheme for ultra-low power neuromorphic hardware using emerging spin-devices. We propose device models for 'neuron', based on lateral spin valves and domain wall magnets that can operate at ultra-low terminal voltage of…
Neural networks and neuromorphic computing play pivotal roles in deep learning and machine vision. Due to their dissipative nature and inherent limitations, traditional semiconductor-based circuits face challenges in realizing ultra-fast…
Recent years have seen an increasing interest in the development of artificial intelligence circuits and systems for edge computing applications. In-memory computing mixed-signal neuromorphic architectures provide promising ultra-low-power…
Emerging non-volatile memories (NVMs) have currently attracted great interest for their potential applications in advanced low-power information storage and processing technologies. Conventional NVMs, such as magnetic random access memory…