https://scholar.dgist.ac.kr/handle/20.500.11750/58914 Wed, 11 Nov 2026 11:15:17 GMT 2026-11-11T11:15:17Z https://scholar.dgist.ac.kr/handle/20.500.11750/61098 Title: Structure-Engineered Nanoporous Vanadium Oxide Memristors for Reconfigurable Synapse-Neuron Integration and Synergistic Robotic Motion Author(s): Park, Gwanyeong; Heo, Si-Hwan; Park, Young Ran; Kim, Mingyu; Choi, Sanghyeon; Song, Chaeyoon; Son, Junwoo; Yang, Sungwook; Wang, Gunuk Abstract: Neuromorphic sensory-to-motor interfaces require compact devices that can combine nonvolatile synaptic weight storage with volatile neuronal firing, yet these functions typically rely on distinct material and circuit mechanisms. Here, we report a structure-engineered VOy/nanoporous VOx heterostructure that enables electrically selectable nonvolatile and volatile switching within a vanadium oxide memristor platform. Annealing-induced interfacial diffusion and oxidation produce an asymmetric stack comprising a crystalline VOy layer that supports threshold insulator-to-metal transition dynamics and an oxygen-vacancy-rich nanoporous VOx region that promotes filamentary conductance modulation. In a 16 & times; 16 crossbar array, identically fabricated cells are reconfigured either as artificial synapses exhibiting multilevel retention and analog long-term potentiation/depression or as artificial neurons producing relaxation-oscillator spiking and diverse neuronal response features. By pairing two cells as a one-synapse-one-neuron unit, the programmed synaptic conductance modulates the neuronal firing frequency and measured current-spike amplitude, thereby linking analog weight storage with spike-based signal generation. Using measured device characteristics, a hardware-informed spiking neural network recognizes rock-paper-scissors images with high accuracy, and its output commands are coupled to a memristive synergistic motor system that drives a robotic hand to generate counter-gestures. These results suggest that structure-engineered nanoporous vanadium oxide memristors can serve as reconfigurable building blocks for neuromorphic sensory-to-motor interfaces. Wed, 30 Sep 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/61098 2026-09-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/59141 Title: Wafer-scale fabrication of memristive passive crossbar circuits for brain-scale neuromorphic computing Author(s): Choi, Sanghyeon; Bezugam, Sai Sukruth; Bhattacharya Tinish; Kwon, Dongseok; Strukov, Dmitri B. Abstract: Memristive passive crossbar circuits hold great promise for neuromorphic computing, offering high integration density combined with massively parallel operation. However, scaling up the integration complexity of such circuits remains challenging due to low device yield, stemming from the intrinsic properties of filamentary switching and limitations in current crossbar fabrication technologies. Here, we report a scalable passive crossbar device technology achieved through a co-design approach for memristors and crossbar structures. The proposed hardware platform is fabricated using CMOS-compatible processes without complex and high-temperature steps, enabling high device yield along with reliable and multibit operation. Importantly, the fabrication process is successfully scaled to a 4-inch wafer, maintaining an average device yield (>similar to 95%) and preserving key switching characteristics. The potential of this platform is showcased by implementing image classification of the fashion MNIST benchmark with an ex-situ trained spiking neural network. We believe that our work represents a significant step toward brain-scale neuromorphic computing systems. Tue, 30 Sep 2025 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/59141 2025-09-30T15:00:00Z