Aligned Carbon Nanotube Synaptic Transistors for Large-Scale Neuromorphic Computing.

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.