Information Sciences Institute , University of Southern California , Marina del Rey , California 90292 , United States.
Ming Hsieh Department of Electrical Engineering , University of Southern California , Los Angeles , California 90089 , United States.
ACS Nano. 2018 Jul 24;12(7):7352-7361. doi: 10.1021/acsnano.8b03831. Epub 2018 Jun 29.
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.
本文提出了一种用于大规模神经形态计算系统的对齐碳纳米管（CNT）突触晶体管。这些器件的突触行为是通过电荷俘获效应实现的，这种效应在基于碳的纳米电子学中很常见。在这项工作中，顶部栅控 CNT 场效应晶体管（FET）的高 k 介电层中的电荷俘获使 CNT 沟道电导的逐渐模拟编程成为可能，具有大的动态范围（即大的导通/关断比）。与传统的忆阻器技术（例如 RRAM）相比，对齐的 CNT 突触器件具有显著的改进，因为传统的忆阻器技术在电导调制方面存在突然的转变，或者动态范围较小。在这里，我们通过脉冲实验证明了对齐的 CNT FET 突触行为具有出色的均匀性，以及显著的鲁棒性和非易失性，从而确立了它们在神经网络实现中的适用性。此外，这项技术基于一种在晶圆级构建高度对齐的 CNT 阵列的技术，具有高半导体纯度，并且完全与 CMOS 兼容，确保了大规模 CNT+CMOS 神经形态系统的实用性。我们还展示了对齐的 CNT 突触行为的微调能力，并讨论了其在自适应在线学习方案和人工神经元发射率的动态平衡调节中的应用。我们使用基于尖峰时间依赖可塑性（STDP）的方案模拟了用于模式识别的无监督学习的实现，指出了系统级性能（如识别精度所示），并证明了通过调整对齐的 CNT 器件的突触特性可以提高学习率。
