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April 16, 2026ACS Applied Materials & Interfaces0 citations

A-Site Cation Functional Engineering Enables Lead-free Perovskite Photosynapse for Neuromorphic Visual Computing

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XMXinyu MaZLZhihao LeiBDBeining Dong

Key Points

  • To introduce a lead-free perovskite that functions as an optoelectronic synapse for sustainable neuromorphic computing.
  • Developed a lead-free antimony halide perovskite, Cs2AgSb2I9
  • Incorporated silver (Ag) into the perovskite lattice
  • Investigated electronic structure and synaptic behaviors of the device
  • Demonstrated ultralow energy consumption of 6.18 × 10-14 J per synaptic event
  • Achieved 97.5% accuracy in image recognition using artificial neural networks
  • Exhibited extensive synaptic behaviors such as paired-pulse facilitation and Hebbian-like learning

Abstract

Optoelectronic synapses that directly couple light sensing with memory and computing functions offer a promising route toward energy-efficient neuromorphic vision systems. However, the development of high-performance perovskite synapses remains heavily reliant on lead-based perovskites, raising sustainability and toxicity concerns. Herein, we report a previously unexplored lead-free antimony halide perovskite, Cs2AgSb2I9, and demonstrate its application as an ultralow-power optoelectronic photosynapse, enabled by A-site cation functional engineering. Specifically, incorporation of Ag into the perovskite lattice fundamentally modulates the electronic structure, suppressing excitonic confinement, enhancing carrier transport, and introducing energetically favorable Ag interstitial states that act as reversible charge trapping/detrapping centers to enable short-term memory. As a result, Cs2AgSb2I9 devices exhibit extensive synaptic behaviors, including paired-pulse facilitation and spike-intensity-, spike-duration-, and spike-number-dependent plasticity, as well as Hebbian-like learning characteristics. Notably, the photosynapse can operate with an ultralow energy consumption of 6.18 × 10-14 J per synaptic event, approaching the energy scale of biological synapses. Artificial neural networks based on Cs2AgSb2I9 devices further demonstrate a high accuracy of 97.5% for image recognition. Overall, this work introduces a new lead-free perovskite platform and highlights A-site cation functional engineering as an effective strategy for realizing sustainable and low-power neuromorphic visual computing.

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Cite This Study

Ma et al. (2026) studied this question.

synapsesocial.com/papers/69e07e3b2f7e8953b7cbf3a6https://doi.org/10.1021/acsami.6c02560
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