Revannath Dnyandeo Nikam 교수

Molybdenum disulfide (MoS2) is a promising catalyst for hydrogen evolution reaction (HER) because of its unique nature to supply active sites in the reaction. However, the low density of active sites and their poor electrical conductivity have limited the performance of MoS2 in HER. In this work, we synthesized MoS2 nanosheets on three-dimensional (3D) conductive MoO2 via a two-step chemical vapor deposition (CVD) reaction. The 3D MoO2 structure can create structural disorders in MoS2 nanosheets

All solid-state lithium-ion transistors are considered as promising synaptic devices for building artificial neural networks for neuromorphic computing. However, the slow ionic conduction in existing electrolytes hinders the performance of lithium-ion-based synaptic transistors. In this study, we systematically explore the influence of ionic conductivity of electrolytes on the synaptic performance of ionic transistors. Isovalent chalcogenide substitution such as Se in Li₃PO<sub>4</sub

Abstract Lithium nanoionic transistors have recently emerged as promising artificial synaptic devices for neuromorphic hardware systems. However, mimicking the essential synaptic functionalities including nonvolatile conductance modulation with a near‐linear analog weight update has been a crucial milestone in those synaptic devices and has a direct impact on pattern recognition accuracy. The volatile channel conductance change due to the instability of the solid electrolyte interface and lithiu

Abstract Artificial synapses based on electrochemical random‐access memory (ECRAM) have emerged as an important component for neuromorphic chips because they are capable to execute simultaneous signal transmission and memory operations. However, existing ECRAM synapse surfers with compatibility and rapid memory loss issue due to highly reactive Li + and H + cationic species. Here, all‐solid‐state oxygen ion‐based ECRAM (O‐ECRAM) synapse, which shows linear weight update characteristics through m

The first report of a quantized conductance atomic threshold switch (QCATS) using an atomically-thin hexagonal boron nitride (hBN) layer is provided. This QCATS has applications in memory and logic devices. The QCATS device shows a stable and reproducible conductance quantization state at 1·G₀ by forming single-atom point contact through a monoatomic boron defect in an hBN layer. An atomistic switching mechanism in hBN-QCATS is confirmed by in situ visualization of mono-atomic conduct

The first report on ion transport through atomic sieves of atomically thin 2D material is provided to solve critical limitations of electrochemical random-access memory (ECRAM) devices. Conventional ECRAMs have random and localized ion migration paths; as a result, the analog switching efficiency is inadequate to perform in-memory logic operations. Herein ion transport path scaled down to the one-atom-thick (≈0.33 nm) hexagonal boron nitride (hBN), and the ionic transport area is confined to a s

Advanced materials and device engineering has played a crucial role in improving the performance of electrochemical random access memory (ECRAM) devices. ECRAM technology has been identified as a promising candidate for implementing artificial synapses in neuromorphic computing systems due to its ability to store analog values and its ease of programmability. ECRAM devices consist of an electrolyte and a channel material sandwiched between two electrodes, and the performance of these devices dep

Abstract This report demonstrates that atomic‐level controlled formation/rupture of conductive filaments using all 2D heterostructure of hBN‐graphene is a feasible way to achieve excellent switching characteristics for ideal atomic switches. At a threshold voltage, graphene with stable ion migration routes forms a few atom comprising Ag filaments in hBN, which subsequently spontaneously break as the applied voltage lowers, resulting in optimal threshold switching behavior in an hBN atomic switch

An artificial synapse based on oxygen-ion-driven electrochemical random-access memory (O-ECRAM) devices is a promising candidate for building neural networks embodied in neuromorphic hardware. However, achieving commercial-level learning accuracy in O-ECRAM synapses, analog conductance tuning at fast speed, and multibit storage capacity is challenging because of the lack of Joule heating, which restricts O^2- ionic transport. Here, we propose the use of an atomically thin heater of mon