Abstract
Neuromorphic computation based on resistive switching devices represents a relevant hardware alternative for artificial deep neural networks. For the highest accuracies on pattern recognition tasks, an analog, linear, and symmetric synaptic weight is essential. Moreover, the resistive switching devices should be integrated with the supporting electronics, such as thin-film transistors (TFTs), to solve crosstalk issues on the crossbar arrays. Here, an a-Indium-gallium-zinc-oxide (IGZO) memristor is proposed, with Mo and Ti/Mo as bottom and top contacts, with forming-free analog switching ability for an upcoming integration on crossbar arrays with a-IGZO TFTs for neuromorphic hardware systems. The development of a TFT compatible fabrication process is accomplished, which results in an a-IGZO memristor with a high stability and low cycle-to-cycle variability. The synaptic behavior through potentiation and depression tests using an identical spiking scheme is presented, and the modulation of the plasticity characteristics by applying non-identical spiking schemes is also demonstrated. The pattern recognition accuracy, using MNIST handwritten digits dataset, reveals a maximum of 91.82% accuracy, which is a promising result for crossbar implementation. The results displayed here reveal the potential of Mo/a-IGZO/Ti/Mo memristors for neuromorphic hardware.
Highlights
Artificial intelligence (AI) is currently the key feature on innovative technologies for smart systems, pushing for breakthroughs in numerous fields, ranging from healthcare to security solutions
In order to maximize the signal from the aIGZO and to obtain a clear analysis of the bottom contact interface, the IGZO films have been measured without the Mo/Ti top contact
It is important to note that several devices were tested for the modulation of the pulse scheme and behave identically within their category, 20Ar/5O2 and 20Ar/20O2, as it can be evaluated in Fig. S5 of the supplementary material, where potentiation and depression tests under the same non-identical pulse schemes for five different devices are presented, proving the reproducibility of the fabrication process and the reliability of the proposed scheme for a linear and symmetric synaptic characteristic of the Mo/a-IGZO/Ti/Mo structure
Summary
Artificial intelligence (AI) is currently the key feature on innovative technologies for smart systems, pushing for breakthroughs in numerous fields, ranging from healthcare to security solutions. To develop an integrated analog controlled weight storage on-chip technology, for a large-scale energy-efficient DNN, the RS devices should be integrated with supporting electronics An active element such as the transistor solves crosstalk issues on the crossbar arrays that occur due to the interference of neighboring cells.. A fully integrated circuit has not been demonstrated where both transistor semiconductor and memristor RS layer share the one and same processing step as well as the electrode materials, which would imply a significant decrease in the total lithography mask count, improved interconnectivity, and drastic cost reduction The reason behind this gap is the fact that TFTs should be optimized for a high stability and low leakage and the RS device should be optimized for a defect-enabled switching ability with high on/off ratio, which results in a contradictory film optimization where compromises must be made. Further energy dispersive spectroscopy (EDS) analysis was undertaken, using a Carl Zeiss AURIGA CrossBeam FIB-SEM workstation, to confirm the XPS results
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