The properties of 3D halide perovskites, such as mixed ionic–electronic conductivity and feasible ion migration, have enabled them to challenge traditional memristive materials. However, issues like poor moisture stability and difficulty in controlling ion transport due to their polycrystalline nature have hindered their use as a neuromorphic hardware. Recently, 2D halide perovskites have emerged as promising artificial synapses owing to their phase versatility, microstructural anisotropy in electrical and optoelectronic properties, and excellent moisture resistance. However, their asymmetrical and nonlinear conductance changes still limit the efficiency of training and accuracy of inference.
Now, researchers from Seoul National University, Korea University, Sungkyunkwan University, Pohang University of Science and Technology and University of Southern California have achieved highly linear and symmetrical conductance changes in Dion–Jacobson 2D perovskites.
The team built a 7 × 7 crossbar array based on analogue perovskite synapses, achieving a high device yield, low variation with synaptic weight storing capability, multi-level analogue states with long retention, and moisture stability over 7 months.
The scientists explored the potential of such devices in large-scale image inference via simulations and showed an accuracy within 0.08% of the theoretical limit.
The excellent device performance is attributed to the elimination of gaps between inorganic layers, allowing the halide vacancies to migrate homogeneously regardless of grain boundaries. This was confirmed by first-principles calculations and experimental analysis.
