SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations
SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations
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DOI：
10.1038/s41563-017-0001-5
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发表时间：
2018-04-01
期刊：
影响因子：
41.2
通讯作者：
Kim, Jeehwan
中科院分区：
文献类型：
--
作者：
Choi, Shinhyun;Tan, Scott H.;Kim, Jeehwan
尽管结合存储单元和晶体管的几种类型的架构已被用于演示人工突触阵列，但它们通常呈现有限的可扩展性和高功耗。无晶体管模拟开关器件可以克服这些限制，但它们所依赖的典型开关过程--在非晶介质中形成灯丝--不容易控制，因此妨碍了性能的空间和时间再现性。在这里，我们展示了模拟电阻开关器件，具有所需的神经形态计算网络的性能变化最小的特性，使用单晶硅锗层外延生长在Si作为开关介质。这种外延随机存取存储器利用SiGe中的穿透位错来将金属丝限制在限定的一维沟道中。这种限制导致极大地增强了开关均匀性和长保持/高耐久性以及高模拟开/关比。使用MNIST手写识别数据集的模拟证明，外延随机存取存储器可以运行的在线学习准确率为95.1%。
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%.