(Invited) Emerging Neuromorphic Devices for Carbon Nanotubes and Related Low-Dimensional Materials

Abstract

The exponentially improving performance of conventional digital computers has slowed in recent years due to the speed and power consumption issues that are largely attributable to the von Neumann bottleneck (i.e., the need to transfer data between spatially separate processor and memory blocks). In contrast, neuromorphic (i.e., brain-like) computing aims to circumvent the limitations of von Neumann architectures by spatially co-locating processor and memory blocks or even combining logic and data storage functions within the same device. In addition to reducing power consumption in conventional computing, neuromorphic devices also provide efficient architectures for emerging applications such as image recognition, machine learning, and artificial intelligence. With this motivation in mind, this talk will explore the opportunities for carbon nanotubes and related low-dimensional materials in neuromorphic devices. For example, by combining p-type semiconducting carbon nanotubes with n-type two-dimensional transition metal dichalcogenides, gate-tunable diodes have been realized, which show anti-ambipolar transfer characteristics that are suitable for artificial neurons, competitive learning, and spiking circuits [1]. In addition, by exploiting field-driven defect motion mediated by grain boundaries in monolayer MoS2, gate-tunable memristive phenomena have been achieved, which enable hybrid memristor/transistor devices (i.e., "memtransistors") that concurrently provide logic and data storage functions [2]. The planar geometry of memtransistors further allows multiple contacts to the channel region that mimic the behavior of biological neurons such as heterosynaptic responses [3]. Overall, this work introduces new foundational circuit elements for neuromorphic computing in addition to providing alternative pathways for studying and utilizing the unique charge transport characteristics of low-dimensional materials [4]. [1] V. K. Sangwan, et al., Nano Letters, 18, 1421 (2018). [2] V. K. Sangwan, et al., Nature Nanotechnology, 10, 403 (2015). [3] V. K. Sangwan, et al., Nature, 554, 500 (2018). [4] D. Jariwala, et al., Nature Materials, 16, 170 (2017).