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<p>The bandgap of two-dimensional black phosphorus can be modulated under a vertical electric field due to the Stark effect. However, its circuit applications remain elusive. Here we utilize the Stark effect in black phosphorus for digital and analogue circuit applications. By modulating the bandgap, we can control the current on/off ratio and intrinsic carrier concentration. This enables the effective tuning of amplifier gain and bandwidth, as well as the realization of both binary and ternary logic gates. Using this effect, we build a black phosphorus amplifier with a current-source load, showing a steep gain-tuning slope and more than an order-of-magnitude bandwidth modulation. Furthermore, we demonstrated a stacked black phosphorus transistor array for binary convolutional neural network with better performance compared with silicon- and memristor-based circuits, highlighting its potential for next-generation electronic systems.</p>

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Reconfigurable and multifunctional circuits using the Stark effect in black phosphorus

  • 期刊论文

  • 发表日期：

    2026年5月25日

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  • He Tian,
  • Zhan Hou,
  • Fan Wu,
  • Jing-Wen Jiang,
  • Dai-Xuan Wu,
  • Yang Shen,
  • Ting-Yi Xu,
  • Xiao-Yong Xue,
  • Zi-Ming Wang,
  • Hao Guo,
  • Tian-Ling Ren

摘要

The bandgap of two-dimensional black phosphorus can be modulated under a vertical electric field due to the Stark effect. However, its circuit applications remain elusive. Here we utilize the Stark effect in black phosphorus for digital and analogue circuit applications. By modulating the bandgap, we can control the current on/off ratio and intrinsic carrier concentration. This enables the effective tuning of amplifier gain and bandwidth, as well as the realization of both binary and ternary logic gates. Using this effect, we build a black phosphorus amplifier with a current-source load, showing a steep gain-tuning slope and more than an order-of-magnitude bandwidth modulation. Furthermore, we demonstrated a stacked black phosphorus transistor array for binary convolutional neural network with better performance compared with silicon- and memristor-based circuits, highlighting its potential for next-generation electronic systems.