Halide perovskites (HPs) have gained significant attention in optoelectronics and photovoltaics due to their tunable bandgap, high color purity, and long charge diffusion lengths. However, the application of HPs in resistive random-access memory (RRAM) has been hindered by material and electrical instability in traditional thin-films resulting in subpar figures-of-merit such as retention, endurance, and switching speed [1, 2].

To address these limitations, a novel switching matrix has been developed that replaces the thin-film architecture with vertically aligned, three-dimensional, high-density monocrystalline HP nanowires and quantum wires. These nanostructures are embedded within a porous alumina membrane (PAM) sandwiched between metal contacts. The PAM provides excellent passivation, imparting crucial electrical and material stability to the environmentally sensitive HPs by drastically reducing surface diffusion pathways and thereby thwarting moisture-induced degradation.

This approach yields record-breaking performance for HP-based RRAMs, with extrapolated retention times of up to 28.3 years, measured endurance of 5 million cycles, and switching speeds as fast as 100 picoseconds. These results represent the best figures-of-merit reported for HP RRAMs and stand in stark contrast to HP thin-film devices, which typically demonstrate a maximum endurance of 10 thousand cycles, retention of 10⁵ seconds, and switching speeds of 10 nanoseconds [3, 4]. Furthermore, the scalability potential is demonstrated through the fabrication of a 14 nm lateral size HP quantum wire RRAM cell and the development of a cross-bar device architecture featuring a unique metal-semiconductor-insulator-metal (MSIM) sneaky path mitigation scheme.

Beyond data storage, this technology pioneers the development of HP-based physical neural networks utilizing nanowires in PAM for low-power, high-precision computing. Operation in two distinct modes: electrochemical metallization (ECM) with a silver top electrode and non-electrochemical metallization (non-ECM) with an indium tin oxide top electrode, enables the control of multi-level conductance states. These states are utilized as synaptic weights in artificial neural networks to perform image processing tasks such as outlining, sharpening, and embossing in the ECM mode [5]. In the non-ECM mode, the multi-level states are used to emulate the cognitive learning model of the Gestalt Closure Principle [6].

The use of HP nanowires embedded in a PAM provides superior material reliability, retention, and jitter of the conduction states compared to thin-film counterparts, enabling the construction of a physical artificial vision system. A physical processor is integrated with a physical pre-processor, resulting in reliable photo-synaptic behavior and stable, temporally robust conduction states. The devices exhibit retention greater than 10⁵ seconds and temporal jitter below 10%, and respond to changes in the intensity, duration, and frequency of light pulses, allowing for functionalities such as contrast enhancement. The system's capability was validated by successfully recognizing four different geometric shapes using a 6×6 array of nanowire devices [7].

In summary, these findings advance HP RRAMs to the state-of-the-art standard and showcase the potential of  perovskite nanowire devices as a compelling alternative technology for future data storage and neuromorphic computing modules.