From light sensing to adaptive learning: hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing

Bashayr Alqahtani; Hanrui Li; Abdul Momin Syed; Nazek El-Atab

doi:10.1038/s41377-024-01698-6

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From light sensing to adaptive learning: hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing

Original Articles

- From light sensing to adaptive learning: hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing
- Light: Science & Applications Vol. 14, Issue 1, Pages: 215-226(2025)
- Affiliations：
  
  1.Electrical and Computer Engineering Program, Computer Electrical Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
  2.Electrical Engineering Department, College of Engineering, Princess Nourah Bint Abdulrahman University (PNU), Riyadh, Saudi Arabia
- Author bio：
  
  Nazek El-Atab (nazek.elatab@kaust.edu.sa)
- Funds：
- DOI：10.1038/s41377-024-01698-6
  CLC：
- Published：31 January 2025，
  
  Published Online：03 January 2025，
  
  Received：27 July 2024，
  
  Revised：26 October 2024，
  
  Accepted：2024-11-20
- Accepted：
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Alqahtani, B. et al. From light sensing to adaptive learning: hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing. Light: Science & Applications, 14, 215-226 (2025).
DOI：

Alqahtani, B. et al. From light sensing to adaptive learning: hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing. Light: Science & Applications, 14, 215-226 (2025). DOI： 10.1038/s41377-024-01698-6.

摘要

Abstract

Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior

dynamic responses

and energy efficiency characteristics. Although charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity

replicating the key functionality of neurons—integrating diverse presynaptic inputs to fire electrical impulses—has remained challenging. In this study

we developed reconfigurable metal-oxide-semiconductor capacitors (MOSCaps) based on hafnium diselenide (HfSe

). The proposed devices exhibit (1) optoelectronic synaptic features and perform separate stimulus-associated learning

indicating considerable adaptive neuron emulation

(2) dual light-enabled charge-trapping and memcapacitive behavior within the same MOSCap device

whose threshold voltage and capacitance vary based on the light intensity across the visible spectrum

(3) memcapacitor volatility tuning based on the biasing conditions

enabling the transition from volatile light sensing to non-volatile optical data retention. The reconfigurability and multifunctionality of MOSCap were used to integrate the device into a leaky integrate-and-fire neuron model within a spiking neural network to dynamically adjust firing patterns based on light stimuli and detect exoplanets through variations in light intensity.

关键词

Keywords

references

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