A Vacuum Gap Selector With Ultra-Low Leakage for Large-Scale Neuromorphic Network

Abstract

In this work, we proposed a thin-film selector with a vacuum gap structure for neuromorphic application, which demonstrates outstanding performance including ultra-low leakage current (~0.20 pA), high ON/OFF ratio (>10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> ), record-high turn-on slope (<; 0.31 mV/dec.), excellent endurance (> 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> ) and low turn-on energy (113.37 pJ). Our selector could minimize sneak currents in crossbar array, enabling terabits scale up capability. Moreover, we have integrated the selector with memristors to form a 2-selector-1-memristor structure and demonstrated several learning rules. These outstanding characteristics indicate that our selector has the potential to enable large scale neuromorphic networks.