Aligned Carbon Nanotube Synaptic Transistors for Large-Scale Neuromorphic Computing

Abstract

This paper presents aligned carbon nanotube (CNT) synaptic transistors for large-scale neuromorphic computing systems. The synaptic behavior of these devices is achieved via charge-trapping effects, commonly observed in carbon-based nanoelectronics. In this work, charge trapping in the high- k dielectric layer of top-gated CNT field-effect transistors (FETs) enables the gradual analog programmability of the CNT channel conductance with a large dynamic range ( i. e., large on/off ratio). Aligned CNT synaptic devices present significant improvements over conventional memristor technologies ( e. g., RRAM), which suffer from abrupt transitions in the conductance modulation and/or a small dynamic range. Here, we demonstrate exceptional uniformity of aligned CNT FET synaptic behavior, as well as significant robustness and nonvolatility via pulsed experiments, establishing their suitability for neural network implementations. Additionally, this technology is based on a wafer-level technique for constructing highly aligned arrays of CNTs with high semiconducting purity and is fully CMOS compatible, ensuring the practicality of large-scale CNT+CMOS neuromorphic systems. We also demonstrate fine-tunability of the aligned CNT synaptic behavior and discuss its application to adaptive online learning schemes and to homeostatic regulation of artificial neuron firing rates. We simulate the implementation of unsupervised learning for pattern recognition using a spike-timing-dependent-plasticity scheme, indicate system-level performance (as indicated by the recognition accuracy), and demonstrate improvements in the learning rate resulting from tuning the synaptic characteristics of aligned CNT devices.

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

“…[123,[140][141][142] Also, FETs based on silicon, organic materials, perovskite or carbon nanotube (CNT) have been reported as synaptic elements based on either charge trapping/detrapping mechanism or floating-gate (FG) memory structure, some of which have a structure of multi-gate FET. [120][121][122][123][124][125][126][127][128] Three-terminal FeFET and two-terminal ferroelectric tunnel junction (FTJ) relying on ferroelectric polarization switching have been explored as artificial synapses, where the pulsing scheme needs to be carefully designed to improve the switching symmetry and linearity. [94,103] Similarly, two-terminal spin-transfer torque MRAM (STT-MRAM) based on magnetization switching and multi-terminal magnetic devices based on domain wall motion have also been studied to implement artificial synapses.…”

Templating Porphyrin Anisotropy via Magnetically Aligned Carbon Nanotubes

“…This paves the way for new applications of these functional materials since the information, partitioned by the magnetic field on the templated chromophore, could be preserved over time. Such hybrid materials could be used to fabricate fieldeffect transistors, [133] solar cells [134] and tailoring charge-transfer processes. [135] Further efforts could be also directed in preparing anisotropic hydrogels and exploiting the mechanical toughness, actuating and electroconductive properties.…”

Recent Progress in Synaptic Devices Paving the Way toward an Artificial Cogni‐Retina for Bionic and Machine Vision

“…Yet another extension of this concept was shown by Esqueda et al They demonstrated synaptic transistors constructed of aligned CNT for large‐scale neuromorphic applications. They utilized charge trapping in the high‐k dielectric layer of top‐gated CNT field‐effect transistors (FETs) to enable gradual analog programmability of the channel conductance with a large dynamic range (i.e., on/off ratio).…”