Although several types of architecture combining memory cells and transistors have been used to demonstrate artificial synaptic arrays, they usually present limited scalability and high power consumption. Transistor-free analog switching devices may overcome these limitations, yet the typical switching process they rely on—formation of filaments in an amorphous medium—is not easily controlled and hence hampers the spatial and temporal reproducibility of the performance. Here, we demonstrate analog resistive switching devices that possess desired characteristics for neuromorphic computing networks with minimal performance variations using a single-crystalline SiGe layer epitaxially grown on Si as a switching medium. Such epitaxial random access memories utilize threading dislocations in SiGe to confine metal filaments in a defined, one-dimensional channel. This confinement results in drastically enhanced switching uniformity and long retention/high endurance with a high analog on/off ratio. Simulations using the MNIST handwritten recognition data set prove that epitaxial random access memories can operate with an online learning accuracy of 95.1%.

尽管已使用几种类型的结合了存储单元和晶体管的架构来演示人工突触阵列，但它们通常呈现出有限的可扩展性和高功耗。无晶体管的模拟开关设备可以克服这些限制，但是它们所依赖的典型开关过程（在非晶态介质中形成细丝）不易控制，因此会妨碍性能的时空再现性。在这里，我们演示了使用外延生长在Si上的单晶SiGe层作为切换介质，具有最小性能变化的，具有神经形态计算网络所需特性的模拟电阻式切换设备。此类外延随机存取存储器利用SiGe中的螺纹位错将金属细丝限制在限定的范围内，一维通道。这种限制导致开关均匀性大大提高，并且具有高的模拟开/关比，从而具有长的保持/高耐久性。使用MNIST手写识别数据集进行的仿真证明，外延随机存取存储器可以以95.1％的在线学习精度运行。