The exotic properties of three-dimensional halide perovskites, such as mixed ionic–electronic conductivity and feasible ion migration, have enabled them to challenge traditional memristive materials. However, the poor moisture stability and difficulty in controlling ion transport due to their polycrystalline nature have hindered their use as a neuromorphic hardware. Recently, two-dimensional (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. Here we achieve highly linear and symmetrical conductance changes in Dion–Jacobson 2D perovskites. We further build 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. We explore the potential of such devices in large-scale image inference via simulations and show 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.

三维卤化物钙钛矿的奇特特性，例如混合离子-电子电导率和可行的离子迁移，使它们能够挑战传统的忆阻材料。然而，由于其多晶性质，水分稳定性差且难以控制离子传输，阻碍了它们作为神经形态硬件的使用。最近，二维 （2D） 卤化物钙钛矿因其相位多功能性、电和光电特性的微结构各向异性以及出色的防潮性而成为有前途的人工突触。然而，它们的不对称和非线性电导变化仍然限制了训练效率和推理的准确性。在这里，我们在 Dion-Jacobson 2D 钙钛矿中实现了高度线性和对称的电导变化。我们进一步构建了基于模拟钙钛矿突触的 7 × 7 横杆阵列，实现了高器件产量、低变异性和突触重量存储能力、多级模拟态和长时间保留的水分稳定性超过 7 个月。我们通过仿真探索了此类设备在大规模图像推理中的潜力，并显示出精度在理论极限的 0.08% 以内。出色的器件性能归因于消除了无机层之间的间隙，使卤化物空位能够均匀迁移，而不受晶界的影响。第一性原理计算和实验分析证实了这一点。