Kim, Seonkwon Im, Seongil Kwak, In Cheol Lee, Jungwha Roe, Dong Gue Ju, Hyunsu Cho, Jeong Ho 2025-07-18T08:31:07Z 2025-07-18T08:31:07Z 2025-07-18 2025-09 0935-9648 https://pubs.kist.re.kr/handle/201004/152814 The von Neumann bottleneck and growing energy demands of conventional computing systems require innovative architectural solutions. Although neuromorphic computing is a promising alternative, implementing efficient on-chip learning mechanisms remains a fundamental challenge. Herein, a novel artificial neural platform is presented that integrates three synergistic components: modulation-optimized presynaptic transistors, threshold switching memristor-based neurons, and adaptive feedback synapses. The platform demonstrates real-time synaptic weight modification through correlation-based learning, effectively implementing Hebbian principles in hardware without requiring extensive peripheral circuitry. Stable device operation and successful implementation of local learning rules are confirmed by systematically characterizing a 6 x 6 array configuration. The experimental results demonstrate a correlation between input-output signals and subsequent weight modifications, establishing a viable pathway toward hardware implementation of Hebbian learning in neuromorphic systems. English WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Hardware Implementation of On-Chip Hebbian Learning Through Integrated Neuromorphic Architecture Article 10.1002/adma.202506920 1 Advanced Materials, v.37, no.38 Advanced Materials 37 38 Y scie scopus 001514470400001 Chemistry, Multidisciplinary Chemistry, Physical Nanoscience & Nanotechnology Materials Science, Multidisciplinary Physics, Applied Physics, Condensed Matter Chemistry Science & Technology - Other Topics Materials Science Physics Article MEMRISTOR MEMORY artificial synapse artificial neuron neuromorphic computing neuromorphic devices on-chip learning