Korea University · 工学
Professor Tukaram D. Dongale's research lab specializes in the development of advanced nanomaterials and resistive switching devices for next-generation neuromorphic computing and low-power memory applications. The lab focuses on designing bio-inspired memristive devices using 2D materials like MXene, transition metal oxides (e.g., nickel cobaltite), and biocompatible polymers such as chitosan and collagen to enable multilevel resistive switching and synaptic plasticity. Key research directions include the engineering of nanomaterials for enhanced switching performance, the fabrication of low-cost, scalable memristive devices using techniques like doctor blading and electrospinning, and the exploration of self-rectifying and selector-based crossbar architectures to overcome critical challenges in resistive memory arrays. The lab’s work bridges materials science, nanoelectronics, and artificial intelligence, aiming to create energy-efficient, brain-inspired computing systems.
Figures are computed from collected data and may differ slightly.
Abstract With the demand for low‐power‐operating artificial intelligence systems, bio‐inspired memristor devices exhibit potential in terms of high‐density memory functions and the emulation of the synaptic dynamics of the human brain. The 2D material MXene attracts considerable interest for use in resistive‐switching memory and artificial synapse devices owing to its excellent physicochemical properties in memristor devices. However, few memristive and synaptic MXene devices that display increa
Here, resistive switching (RS) devices are fabricated using naturally abundant, nontoxic, biocompatible, and biodegradable biomaterials. For this purpose, 1D chitosan nanofibers (NFs), collagen NFs, and chitosan-collagen NFs are synthesized by using an electrospinning technique. Among different NFs, the collagen-NFs-based device shows promising RS characteristics. In particular, the optimized Ag/collagen NFs/fluorine-doped tin oxide RS device shows a voltage-tunable analog memory behavior and go
Abstract High-density memory devices are essential to sustain growth in information technology (IT). Furthermore, brain-inspired computing devices are the future of IT businesses such as artificial intelligence, deep learning, and big data. Herein, we propose a facile and hierarchical nickel cobaltite (NCO) quasi-hexagonal nanosheet-based memristive device for multilevel resistive switching (RS) and synaptic learning applications. Electrical measurements of the Pt/NCO/Pt device show the electrof
The recent progress of selector and self‐rectifying devices for resistive random‐access memory applications is reviewed. In particular, the performance of crossbar arrays based on resistive switching (RS) devices, the sneak‐path current issue, and possible solutions is discussed. The parameters and requirements of selector devices are elucidated here, and several types of selector devices, such as a transistor‐assisted transistor‐one resistor, unipolar one diode‐one resistor, bipolar one selecto
Since the discovery of graphene, two-dimensional (2D) materials have gained widespread attention, owing to their appealing properties for various technological applications. Etched from their parent MAX phases, MXene is a newly emerged 2D material that was first reported in 2011. Since then, a lot of theoretical and experimental work has been done on more than 30 MXene structures for various applications. Given this, in the present review, we have tried to cover the multidisciplinary aspects of
The memristive device is a fourth fundamental circuit element with inherent memory, nonlinearity, and passivity properties. Herein, we report on a cost-effective and rapidly produced ZnO thin film memristive device using the doctor blade method. The active layer of the developed device (ZnO) was composed of compact microrods. Furthermore, ZnO microrods were well spread horizontally and covered the entire surface of the fluorine-doped tin oxide substrate. X-ray diffraction (XRD) results confirmed
Open papers in the app to read, cite, and organize with AI.
