2023-12-20 ノースウェスタン大学
◆これまでの研究では同様の戦略が脳のようなコンピューティングデバイスを開発してきましたが、これらのトランジスタは極低温でしか機能しなかったのに対し、新しいデバイスは室温で安定して動作します。その上、高速でエネルギーをほとんど消費せず、電源を切っても情報を保持します。この研究は、自然に存在するモアレパターンを利用した物理学の進展に基づいています。
＜関連情報＞
- https://news.northwestern.edu/stories/2023/12/new-brain-like-transistor-mimics-human-intelligence/
- https://www.nature.com/articles/s41586-023-06791-1
室温でニューロモルフィック機能を発揮するモアレ型シナプストランジスタを開発 Moiré synaptic transistor with room-temperature neuromorphic functionality
Xiaodong Yan,Zhiren Zheng,Vinod K. Sangwan,Justin H. Qian,Xueqiao Wang,Stephanie E. Liu,Kenji Watanabe,Takashi Taniguchi,Su-Yang Xu,Pablo Jarillo-Herrero,Qiong Ma & Mark C. Hersam
Nature Published:20 December 2023
DOI:https://doi.org/10.1038/s41586-023-06791-1
Abstract
Moiré quantum materials host exotic electronic phenomena through enhanced internal Coulomb interactions in twisted two-dimensional heterostructures1,2,3,4. When combined with the exceptionally high electrostatic control in atomically thin materials5,6,7,8, moiré heterostructures have the potential to enable next-generation electronic devices with unprecedented functionality. However, despite extensive exploration, moiré electronic phenomena have thus far been limited to impractically low cryogenic temperatures9,10,11,12,13,14, thus precluding real-world applications of moiré quantum materials. Here we report the experimental realization and room-temperature operation of a low-power (20 pW) moiré synaptic transistor based on an asymmetric bilayer graphene/hexagonal boron nitride moiré heterostructure. The asymmetric moiré potential gives rise to robust electronic ratchet states, which enable hysteretic, non-volatile injection of charge carriers that control the conductance of the device. The asymmetric gating in dual-gated moiré heterostructures realizes diverse biorealistic neuromorphic functionalities, such as reconfigurable synaptic responses, spatiotemporal-based tempotrons and Bienenstock–Cooper–Munro input-specific adaptation. In this manner, the moiré synaptic transistor enables efficient compute-in-memory designs and edge hardware accelerators for artificial intelligence and machine learning.
