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January 22, 2026Light Science & Applications0 citationsOpen Access

A near-infrared Sn-Pb perovskite imager with monolithic integration

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CGCiyu GeCDChengjie DengJZJiaxing Zhu

Key Points

  • The research aims to develop a Sn-Pb perovskite imager with improved optoelectronic properties and integration capabilities.
  • Implemented a Sn(SCN)2 surface passivation strategy
  • Evaluated dark current density and specific detectivity
  • Integrated with a complementary metal-oxide-semiconductor readout circuit
  • Assessed imaging capabilities with a 640 × 512 pixel array
  • Achieved dark current density of 10 nA cm−2 at −0.1 V
  • Reported specific detectivity of ~1.6 × 10 13 Jones
  • External quantum efficiency of 76% at 940 nm
  • Demonstrated advanced material recognition including liquid identification

Abstract

Abstract Solution-processed Sn-Pb perovskites have emerged as promising candidates for near-infrared (NIR) photodetectors due to their low-cost, tunable bandgap and scalable fabrication. However, Sn 2+ oxidation creates Sn vacancies and undesirable p-type doping, resulting in high dark current and limited detectivity, which hinder the practical deployment of Sn-Pb perovskite photodetectors. Herein, we propose a Sn(SCN) 2 inorganic molecular surface passivation strategy to suppress Sn 2+ oxidation, significantly reduce surface defect density and enhance the optoelectronic properties (a dark current density of 10 nA cm −2 at a bias of −0.1 V and a high specific detectivity of ~1.6 × 10 13 Jones). Leveraging this approach, we report the monolithically integrated Sn-Pb perovskite NIR imager with a complementary metal-oxide-semiconductor readout circuit. The imager, featuring a 640 × 512 pixel array with a 15 μm pixel pitch, achieves an external quantum efficiency of 76% at 940 nm and a modulation transfer function of 206.5 LW/PH at 50%. Furthermore, the Sn-Pb perovskite imager demonstrates advanced material recognition capabilities, including liquid identification, underscoring its potential in chemical sensing, biomedical imaging and industrial inspection.

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Cite This Study

Ge et al. (2026) studied this question.

synapsesocial.com/papers/6971bd6a642b1836717e21b2https://doi.org/10.1038/s41377-025-02127-y
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