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基于天然生物材料和碳纳米管的可降解光子突触晶体管。

Degradable Photonic Synaptic Transistors Based on Natural Biomaterials and Carbon Nanotubes.

机构信息

Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China.

出版信息

Small. 2021 Mar;17(10):e2007241. doi: 10.1002/smll.202007241. Epub 2021 Feb 15.

DOI:10.1002/smll.202007241
PMID:33590701
Abstract

Artificial synaptic devices have potential for overcoming the bottleneck of von Neumann architecture and building artificial brain-like computers. Up to now, developing synaptic devices by utilizing biocompatible and biodegradable materials in electronic devices has been an interesting research direction due to the requirements of sustainable development. Here, a degradable photonic synaptic device is reported by combining biomaterials chlorophyll-a and single-walled carbon nanotubes (SWCNTs). Several basic synaptic functions, including excitatory postsynaptic current (EPSC), paired pulse facilitation (PPF), transition from short-term memory (STM) to long-term memory (LTM), and learning and forgetting behaviors, are successfully emulated through the chlorophyll-a/SWCNTs synaptic device. Furthermore, decent synaptic behaviors can still be achieved at a low drain voltage of -0.0001 V, which results in quite low energy consumption of 17.5 fJ per pulse. Finally, the degradability of this chlorophyll-a/SWCNTs transistor array is demonstrated, indicating that the device can be environmentally friendly. This work provides a new guide to the development of next-generation green and degradable neuromorphic computing electronics.

摘要

人工突触器件具有克服冯·诺依曼架构瓶颈和构建类脑人工计算机的潜力。到目前为止,由于可持续发展的要求,利用电子设备中生物相容性和可生物降解的材料来开发突触器件已经成为一个有趣的研究方向。在这里,通过结合生物材料叶绿素-a 和单壁碳纳米管 (SWCNTs),报道了一种可降解的光突触器件。通过叶绿素-a/SWCNTs 突触器件,成功模拟了几个基本的突触功能,包括兴奋性突触后电流 (EPSC)、成对脉冲易化 (PPF)、从短期记忆 (STM) 到长期记忆 (LTM) 的转变,以及学习和遗忘行为。此外,在低至 -0.0001 V 的漏极电压下仍能实现良好的突触行为,从而使每个脉冲的能耗低至 17.5 fJ。最后,证明了这种叶绿素-a/SWCNTs 晶体管阵列的可降解性,表明该器件具有环保性。这项工作为开发下一代绿色可降解神经形态计算电子学提供了新的指导。

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