Park, Young Ran Wang, Gunuk 2026-03-27T01:00:35Z 2026-03-27T01:00:35Z 2026-03-24 2026-02 https://pubs.kist.re.kr/handle/201004/154475 The ability of multitasking (MT) learning in neuro-inspired artificial intelligence (AI) systems offers promise for energy-efficient deployment in robotics, health care, and autonomous vehicles. Here, an MT learning framework is established using a dual-output electroluminescent synaptic device array based on a mixed-dimensional stacked configuration with Cs1−xFAxPbBr3 (0.00 ≤ x ≤ 0.15) quantum dots. The device concurrently processes postsynaptic current (PSC) and postsynaptic electroluminescence (PSEL) signals, demonstrating stable and adjustable long-term plasticity with ~1000 individual states, along with spike rate-dependent plasticity and paired-pulse facilitation. By synthesizing the update behavior of both PSC and PSEL pathways, the MT framework simultaneously executes classification-regression and classification-image reconstruction tasks. This approach achieves computational speed improvements of up to 47.09 and 29.17% while reducing energy consumption by up to 8.2- and 32.4-fold compared to a combined single-tasking framework and graphics processing unit–based hardware accelerators, respectively. This innovative method emphasizes the potential of dual-output electroluminescent artificial synapse for MT learning applications. English American Association for the Advancement of Science Electroluminescent perovskite QD–based neural networks for energy-efficient and accelerate multitasking learning Article 10.1126/sciadv.ady8518 1 Science Advances, v.12, no.8 Science Advances 12 8 Y scie scopus 001696389500025 2-s2.0-105030785980 Multidisciplinary Sciences Science & Technology - Other Topics Article HIGH-PERFORMANCE CLASSIFICATION MEMRISTOR SYSTEMS