Seung Ju Kim is a Postdoctoral Researcher at the University of Southern California. He received his B.S., M.S., and Ph.D. degrees in Materials Science and Engineering from Seoul National University. His research focuses on iontronic materials, memristive devices, and neuromorphic hardware, spanning materials and interface engineering, device physics, reliability, and system-level integration. By connecting ionic and electronic transport at the materials level with device and system functionalities, he aims to develop adaptive and energy-efficient hardware for neuromorphic computing, edge intelligence, and intelligent sensing. His work has been recognized through honors including selection as a Rising Star in IOP Publishing’s Neuromorphic Computing and Engineering, the NIH BRAIN Initiative NeuroAI Early Career Scholar Award, selection to participate in the 73rd Lindau Nobel Laureate Meeting, and national dissertation and fellowship awards.

Iontronic materials and memristive devices provide a versatile platform for integrating memory, sensing, and computation through coupled ionic and electronic dynamics[1]. This seminar presents a series of studies spanning materials engineering, device physics, modeling, and system-level implementation. Halide perovskite memristors are explored as nonvolatile drift-type devices[2], with emphasis on linear programmability[3], reliable switching[4], and flexible ReRAM arrays[5]. In parallel, in-situ-processed diffusive memristors are developed for reliable operation, while their underlying switching mechanisms are systematically investigated[6]. Physics-based models derived from these device characteristics enable circuit-level implementation and SPICE-based exploration of nonlinear dynamics, including operation near the edge of chaos[7]. The complementary functionalities of nonvolatile drift and volatile diffusive memristors are further integrated with self-powered sensors to realize multimodal sensor fusion and neuromorphic information processing[8]. Together, these studies demonstrate a pathway from iontronic materials and device physics toward adaptive, energy-efficient intelligent hardware.

1. Kim, S. J., et al. Memristive Ion Dynamics to Enable Biorealistic Computing. Chem. Rev. 125, 745–785 (2025). https://doi.org/10.1021/acs.chemrev.4c00587
2. Kim, S. J., et al. Halide Perovskites for Neuromorphic Sensing and Computing. ACS Appl. Mater. Interfaces 17, 59951–59978 (2025). https://doi.org/10.1021/acsami.5c11432
3. Kim, S. J. et al. Linearly programmable two-dimensional halide perovskite memristor arrays for neuromorphic computing. Nat. Nanotechnol. 20, 83–92 (2025). https://doi.org/10.1038/s41565-024-01790-3
4. Kim, S. J. et al. Vertically aligned two-dimensional halide perovskites for reliably operable artificial synapses. Materials Today 52, 19–30 (2022). https://doi.org/10.1016/j.mattod.2021.10.035
5. Kim, S. J. et al. Reliable and Robust Two-Dimensional Perovskite Memristors for Flexible-Resistive Random-Access Memory Array. ACS Nano 18, 28131–28141 (2024). https://doi.org/10.1021/acsnano.4c07673
6. Kim, S. J. et al. Moisture effects on diffusive memristors. Neuromorphic Computing and Engineering (2026) https://doi.org/10.1088/2634-4386/ae9982
7. Kim, S. J. et al. Diffusive memristors in the edge of chaos. Nat. Commun. 17, 7920 (2026). https://doi.org/10.1038/s41467-026-74640-6
8. Kim, S. J. et al. Self-powered analogue neuromorphic system for multimodal sensing, encoding and learning with diffusive and drift memristors. Nature Sensors 1, 535–544 (2026). https://doi.org/10.1038/s44460-026-00067-7