Novel synaptic memory device for neuromorphic computing

• DOI: 10.1038/srep05333
• Publication Date: 2014-06-18T09:18:43Z
• Authors: Saptarshi Mandal; Ammaarah El-Amin; Kaitlyn Alexander; Bipin Rajendran; Rashmi Jha
• Keywords: Neurons; Manganese; Timing Dependent Plasticity; Models, Neurological; Systems; Computational Biology; Neurophysiology; Oxides; 02 engineering and technology; Article; Memory; Synapses; Animals; Humans; Neural Networks, Computer; 0210 nano-technology; Algorithms; Hafnium

This report discusses the electrical characteristics of two-terminal synaptic memory devices capable of demonstrating an analog change in conductance in response to the varying amplitude and pulse-width of the applied signal. The devices are based on Mn doped HfO₂ material. The mechanism behind reconfiguration was studied and a unified model is presented to explain the underlying device physics. The model was then utilized to show the application of these devices in speech recognition. A comparison between a 20 nm × 20 nm sized synaptic memory device with that of a state-of-the-art VLSI SRAM synapse showed ~10× reduction in area and >10(6) times reduction in the power consumption per learning cycle.