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%.
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This work is partially supported by NSF-SRC-E2CDA under contract no. 2018-NC-2762B. We thank J. J. Yang, Q. Xia, P. Lin, Y. Li, M. Rao and Y. Zhuo of the University of Massachusetts for valuable help and fruitful discussion. We also thank S. Kim of the IBM T.J. Watson Research Center for valuable suggestions for experiments. This work was performed in part at the Micro Technology Laboratories (MTL) at the Massachusetts Institute of Technology, and in part at the Harvard University Center for Nanoscale Systems (CNS), supported by the National Science Foundation under NSF ECCS award no. 1541959.
The authors declare no competing financial interests.
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Choi, S., Tan, S.H., Li, Z. et al. SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations. Nature Mater 17, 335–340 (2018). https://doi.org/10.1038/s41563-017-0001-5
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