Journal Article

SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations

Tan, Scott H, Kim, Yunjo, Choi, Chanyeol, Yu, Shimeng, Choi, Shinhyun, Li, Zefan, Hanwool Yeon, Kim, Jeehwan, Pai-Yu, Chen

2018

Overview

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%.

• Publisher: Nature Publishing Group
• Subject: Computation / Computer memory / Computer simulation / Distance learning / Endurance / Engineering / Epitaxial growth / Filaments / Handwriting / Handwriting recognition / High performance computing / High performance systems / Memory / Metal fibers / Neuromorphic computing / Power consumption / Random access / Reproducibility / Semiconductor devices / Silicon germanides / Silicon substrates / Single crystals / Switching theory / Threading dislocations / Transistors