There is an increased demand for next-generation memory devices with high density and fast operation speed to replace conventional memory devices. Memristors are promising candidates for next-generation memory devices because of their scalability, stable data retention, low power consumption, and fast operation. Among the various types of memristors, halide perovskites exhibit potential as emerging materials for memristors by using hysteresis based on the movement of defects or ions in halide perovskites. However, research on the implementation of perovskite materials as memristors is in its early stages; some challenges and problems must be solved to enable the practical application of halide perovskites for next-generation memory devices. From this perspective, we highlight the recent progress in memristors that use halide perovskites. Moreover, we introduce a strategy to enhance the performance and analyze the operation mechanism of memory devices that use halide perovskites. Finally, we summarize the challenges in the development of device technology to use halide perovskites in next-generation memory devices.

Organic–inorganic halide perovskites (OIHPs) have emerged as an active layer for resistive switching memory (RSM). Among various OIHPs, two-dimensional OIHPs are advantageous in RSMs because of their stability. This stability can be further improved using two-dimensional Dion–Jacobson OIHPs. Moreover, OIHP-based RSMs operated by the formation of halide-ion filaments are affected by grain boundaries because they can act as a shortcut for ion migration. Therefore, it is essential to control the grains in OIHPs for reliable memory operation. Here, we present RSMs using Dion–Jacobson OIHP with controlled grain sizes. The grain sizes of the OIHP are effectively controlled by adjusting the ratio of the N,N-dimethylformamide and dimethyl sulfoxide. The controlled grain sizes can modulate the paths for halide ion migration, which enables the change of the on/off ratio in RSM. In addition, cross-point array structure is essential for high-density memory applications. However, in the cross-point array structure, unwanted current flow through unselected memory cells can happen due to sneak-current paths, so it is necessary to suppress leakage current from neighboring cells by adopting selector devices. We demonstrate the application of selector devices to OIHP-based RSMs to prevent sneak current paths. These results provide the potential of OIHP for use in high-density memory applications.

Artificial synapse is the basic unit of a neuromorphic computing system. However, there is a need to explore suitable synaptic devices for the emulation of synaptic dynamics. This study demonstrates a photonic multimodal synaptic device by implementing a perovskite quantum dot charge-trapping layer in the organic poly(3-hexylthiophene-2,5-diyl) (P3HT) channel transistor. The proposed device presents favorable band alignment that facilitates spatial separation of photogenerated charge carriers. The band alignment serves as the basis of optically induced charge trapping, which enables nonvolatile memory characteristics in the device. Furthermore, high photoresponse and excellent synaptic characteristics, such as short-term plasticity, long-term plasticity, excitatory postsynaptic current, and paired-pulse facilitation, are obtained through gate voltage regulation. Photosynaptic characteristics obtained from the device showed a multiwavelength response and a large dynamic range (∼10 3 ) that is suitable for realizing a highly accurate artificial neural network. Moreover, the device showed nearly linear synaptic weight update characteristics with incremental depression electric gate pulse. The simulation based on the experimental data showed excellent pattern recognition accuracy (∼85%) after 120 epochs. The results of this study demonstrate the feasibility of the device as an optical synapse in the next-generation neuromorphic system.