Hardware neural networks could perform certain computational tasks orders of magnitude more energy-efficiently than conventional computers. Artificial neurons are a key component of these networks and are currently implemented with electronic circuits based on capacitors and transistors. However, artificial neurons based on memristive devices are a promising alternative, owing to their potentially smaller size and inherent stochasticity. But despite their promise, demonstrations of memristive artificial neurons have so far been limited. Here we demonstrate a fully on-chip artificial neuron based on microscale electrodes and halide perovskite semiconductors as the active layer. By connecting a halide perovskite memristive device in series with a capacitor, the device demonstrates stochastic leaky integrate-and-fire behavior, with an energy consumption of 20 to 60 pJ per spike, lower than that of a biological neuron. We simulate populations of our neuron and show that the stochastic firing allows the detection of sub-threshold inputs. The neuron can easily be integrated with previously-demonstrated halide perovskite artificial synapses in energy-efficient neural networks.

翻译：硬件神经网络在执行特定计算任务时，其能效可比传统计算机高出数个数量级。人工神经元是这类网络的关键组成部分，目前主要通过基于电容器和晶体管的电子电路实现。然而，基于忆阻器件的人工神经元因其潜在的更小尺寸和固有随机性而成为一种前景广阔的替代方案。但尽管前景看好，迄今为止忆阻人工神经元的实际演示仍然有限。本文展示了一种完全基于芯片的人工神经元，其采用微尺度电极和卤化物钙钛矿半导体作为活性层。通过将卤化物钙钛矿忆阻器件与电容器串联，该器件表现出随机泄漏积分-发放行为，每次脉冲的能量消耗为20至60皮焦耳，低于生物神经元的能耗。我们模拟了该神经元群体，并证明其随机发放特性能够检测亚阈值输入。该神经元可轻松与先前已演示的卤化物钙钛矿人工突触集成，构建高能效神经网络。