Litcius/Paper detail

Controlled Majority-Inverter Graph Logic With Highly Nonlinear, Self-Rectifying Memristor

Run Ni, Ling Yang, Xiaodi Huang, Sheng‐Guang Ren, Tianqing Wan, Yi Li, Xiangshui Miao

2021IEEE Transactions on Electron Devices29 citationsDOI

Abstract

In this article, for the first time, self-rectifying memristors are exploited for logic-in-memory computation. We report a Pt/TaO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> /Ta memristor with salient self-rectifying bipolar features (10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ON-/ OFF-ratio, 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> rectification ratio, 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> nonlinearity, and ~1 pA leakage current), which could support a large passive crossbar array up to 160 Mb with the premise of 10% read margin. Moreover, we propose and experimentally validate a controlled majority-inverter graph logic method based on the self-rectifying switching behaviors, with advantages in computation complexity. Our work is a step forward toward in-memory computing in high-density or even 3-D memristor architectures.

Topics & Concepts

RectificationMemristorComputationNonlinear systemComputer scienceAlgorithmTopology (electrical circuits)Electrical engineeringPhysicsEngineeringVoltageQuantum mechanicsAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesNeuroscience and Neural Engineering