Choi Shinhyun, Tan Scott H, Li Zefan, Kim Yunjo, Choi Chanyeol, Chen Pai-Yu, Yeon Hanwool, Yu Shimeng, Kim Jeehwan
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Nat Mater. 2018 Apr;17(4):335-340. doi: 10.1038/s41563-017-0001-5. Epub 2018 Jan 22.
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%.
尽管已经使用了几种将存储单元和晶体管结合起来的架构来展示人工突触阵列，但它们通常具有有限的可扩展性和高功耗。无晶体管模拟开关器件可能会克服这些限制，然而它们所依赖的典型开关过程——在非晶介质中形成细丝——不易控制，因此阻碍了性能的空间和时间再现性。在这里，我们展示了一种模拟电阻式开关器件，该器件使用外延生长在硅上的单晶SiGe层作为开关介质，具有对神经形态计算网络所需的特性，且性能变化最小。这种外延随机存取存储器利用SiGe中的穿通位错将金属细丝限制在一个定义好的一维通道中。这种限制导致开关均匀性大幅提高，具有长保留时间/高耐久性以及高模拟开/关比。使用MNIST手写识别数据集进行的模拟证明，外延随机存取存储器可以以95.1%的在线学习准确率运行。
