Two-dimensional materials could play an important role in beyond-CMOS (complementary metal–oxide–semiconductor) electronics, and the development of memristors for information storage and neuromorphic computing using such materials is of particular interest. However, the creation of high-density electronic circuits for complex applications is limited due to low device yield and high device-to-device variability. Here, we show that high-density memristive crossbar arrays can be fabricated using hexagonal boron nitride as the resistive switching material, and used to model an artificial neural network for image recognition. The multilayer hexagonal boron nitride is deposited using chemical vapour deposition, and the arrays exhibit a high yield (98%), low cycle-to-cycle variability (1.53%) and low device-to-device variability (5.74%). The devices exhibit different switching mechanisms depending on the electrode material used (gold for bipolar switching and silver for threshold switching), as well as characteristics (such as large dynamic range and zeptojoule-order switching energies) that make them suited for application in neuromorphic circuits.

二维材料可能会在CMOS（互补金属氧化物半导体）电子学中发挥重要作用，使用这种材料开发用于信息存储和神经形态计算的忆阻器尤为重要。然而，由于低器件成品率和高器件间差异性，限制了用于复杂应用的高密度电子电路的创建。在这里，我们表明可以使用六方氮化硼作为电阻切换材料来制造高密度忆阻交叉开关阵列，并将其用于建模用于图像识别的人工神经网络。使用化学气相沉积法沉积多层六方氮化硼，并且阵列显示出高产率（98％），低的周期间可变性（1.53％）和低的设备间可变性（5.74％）。