Knowledge gaps for neuromorphic ionic computing

• 成果类型: Review
• 署名作者: Aluru, Narayana R.; Darling, Seth B.; Elam, Jeffrey W.; Gang, Oleg; Salleo, Alberto; Siwy, Zuzanna; Talin, A. Alec; Noy, Aleksandr
• 署名单位: University of Texas System; University of Texas Austin; United States Department of Energy (DOE); Argonne National Laboratory; University of Chicago; United States Department of Energy (DOE); Argonne National Laboratory; Columbia University; Columbia University; United States Department of Energy (DOE); Brookhaven National Laboratory; United States Department of Energy (DOE); Stanford University; SLAC National Accelerator Laboratory; Stanford University; University of California System; University of California Irvine; United States Department of Energy (DOE); Sandia National Laboratories; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; University of California System; University of California Merced
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-8075; 1095-9203
• DOI: 10.1126/science.aea2097
• 发表日期: 2026-05-07
• 关键词: thermal-conductivity; memory; dna; transport; NANOFABRICATION; channels; circuit; network; spiking; synapse

摘要

Neuromorphic ionic computing is inspired by the brain's use of ions for ultralow-energy computation-its massive parallelism, adaptability, and learning capabilities. This emerging paradigm can overcome limitations of conventional silicon-based computing by enabling colocated memory and processing, multicarrier information streams, and massive three-dimensional connectivity. However, substantial knowledge gaps remain in understanding and engineering ionic transport, energy dissipation, materials design, and scalable device architectures. This Review explores these critical challenges across seven key domains, highlighting the need for new theoretical approaches, materials, device concepts, and fabrication strategies. We argue that advancing ionic neuromorphic systems requires an interdisciplinary approach, integrating insights from biology and neuroscience, nanofluidics, materials science, and systems engineering to enable a new class of energy-efficient, robust, and reconfigurable computing technologies.