Scholarlink

<p>Artificial neural network-based machine learning provides foundations for artificial intelligence (AI), yet requires high energy costs for training. Beyond software-level simulation of neural networks, hardware-level implementation via neuromorphic devices becomes the next milestone in nanoscience towards energy-sustainable AI. Single-molecule devices have the potential for ultimate scale and energy efficiency, but challenges remain in achieving programmable multi-conductance states amidst room-temperature thermal fluctuations. Here we fabricated a bio-inspired single-molecule neuromorphic device consuming ~6.34 aJ/operation by electrochemically gating molecule-ion electrostatic interactions. This device realizes biomimetic emulation of neural plasticity from short-term to long-term memory featuring over 10 distinct conductance states, demonstrating the applications in Pavlovian conditioning for associative learning and pattern recognition in Morse code processing. Our approach enables multi-state synaptic emulation using an individual molecule toward energy-sustainable AI.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Single-molecule neuromorphic device with aJ-level power consumption per switching

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月31日

查看全文
  • Hua Zhang,
  • Jingyao Ye,
  • Mingbin Gao,
  • Chenshuai Yan,
  • Yiqiang Jiang,
  • Bei Zhang,
  • Yu Zhou,
  • Wansong Shang,
  • Liangliang Chen,
  • Jiayi Wu,
  • Zhi Li,
  • Tianyue Zeng,
  • Wei Xu,
  • Xiaohui Li,
  • Jie Bai,
  • Jing Li,
  • Yanxi Zhang,
  • Zongyuan Xiao,
  • Jia Shi,
  • Guanxin Zhang,
  • Junyang Liu,
  • Deqing Zhang,
  • Wenjing Hong

摘要

Artificial neural network-based machine learning provides foundations for artificial intelligence (AI), yet requires high energy costs for training. Beyond software-level simulation of neural networks, hardware-level implementation via neuromorphic devices becomes the next milestone in nanoscience towards energy-sustainable AI. Single-molecule devices have the potential for ultimate scale and energy efficiency, but challenges remain in achieving programmable multi-conductance states amidst room-temperature thermal fluctuations. Here we fabricated a bio-inspired single-molecule neuromorphic device consuming ~6.34 aJ/operation by electrochemically gating molecule-ion electrostatic interactions. This device realizes biomimetic emulation of neural plasticity from short-term to long-term memory featuring over 10 distinct conductance states, demonstrating the applications in Pavlovian conditioning for associative learning and pattern recognition in Morse code processing. Our approach enables multi-state synaptic emulation using an individual molecule toward energy-sustainable AI.