Publication date: 24th July 2026
With the exponential growth of artificial intelligence, big data, and the internet of things, there is an urgent need for high-speed, energy-efficient computing technologies. Neuromorphic computing meets this demand by utilizing hardware architectures capable of massively parallel processing with ultra-low power consumption. Such architectures have traditionally been based on complementary metal-oxide semiconductor circuits; however, scaling up computational performance now requires emerging devices that deliver high-order complexity while remaining highly area- and energy-efficient [1]. Recently, optoelectronic devices based on organic semiconductor and two-dimensional (2D) halide perovskite have emerged for neuromorphic computing due to their attractive optoelectronic properties and low-cost processability. By coupling the high photosensitivity of 2D halide perovskite and good charge transport of organic semiconductors, the resulting interface heterojunction allows for the highly tunable optical modulation of charge-trapping effects, ultimately realizing a robust, nonvolatile photomemory [2]. Herein, a nonvolatile memory device based on a 2D Ruddlesden-Popper-phase perovskite and a p-type organic semiconductor heterojunction is fabricated. The synergy of this heterojunction is critical: the organic layer serves as the primary charge transport channel, while the 2D perovskite’s intrinsic quantum well acts as a charge-trapping layer to capture and store carriers under applied bias. The carrier transport and storage properties during optical gating are optimized, and the memory properties can be readily modulated. The memory device exhibits hysteresis transfer curves with a memory window of 30 V and a current switching ratio of 103. The results validate the potential of 2D perovskite as a highly effective charge-storage medium, paving the way for fast, low-power optical multi-bit storage for future neuromorphic computing systems.
This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 101203243.
