Ion Intercalation‐Enabled Reconfigurable Photodetector with Low Programming Voltage and Broadband Response Based on Van Der Waals Tin Disulfide

Artificial neural networks with integrated sensing and computing capabilities, leveraging reconfigurable optoelectronics, can effectively emulate biological neural networks, thereby enabling rapid and efficient information processing. However, realizing reconfigurable photoresponsivity is often blocked by the requirement for high programming voltages and the limits of the detection spectrum range. This greatly restricts the progress of energy-efficient and precise neuromorphic vision sensing. Herein, a reconfigurable photodetector with low programming voltage and broadband response is presented via in situ intercalation of Cu<sup>+</sup> ions into the van der Waals (vdW) gaps of thermoelectric 2D material SnS<sub>2</sub>. Interestingly, the vdW gaps provide an ionic transport channel with lower energy barriers compared to oxide-based memristors, resulting in a low programming voltage (0.5 V). Furthermore, reversible conversion of photo-detection is achieved from photovoltaic to photo-thermoelectric (PTE) mode via voltage-controlled ion distribution, which modulates the phonon scattering rate in the neighboring SnS<sub>2</sub> layer. As a result, the response spectrum switches from visible (532 nm) to long-wave infrared (10 µm) with an on/off ratio as high as 10<sup>4</sup>. Thus, dual-mode conversion and broadband detection functionality in reconfigurable imaging are realized, suggesting a potential pathway for the development of highly energy-efficient reconfigurable optoelectronics with a spectrum far beyond human vision.

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Publication Details

Journal
Small
Published
2024-12-29
DOI
https://doi.org/10.1002/smll.202408545
Citations
2
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.42

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article

Ion Intercalation‐Enabled Reconfigurable Photodetector with Low Programming Voltage and Broadband Response Based on Van Der Waals Tin Disulfide

Qisheng Wang, Kangmin Leng, Li Wang, Yu Wan et al.
2 citations
Small
Advanced Memory and Neural Computing
0.42
article

Ion Intercalation‐Enabled Reconfigurable Photodetector with Low Programming Voltage and Broadband Response Based on Van Der Waals Tin Disulfide

Qisheng Wang, Kangmin Leng, Li Wang, Yu Wan, Yao Fu, Xin Wang
article en
2 citations

Abstract

Artificial neural networks with integrated sensing and computing capabilities, leveraging reconfigurable optoelectronics, can effectively emulate biological neural networks, thereby enabling rapid and efficient information processing. However, realizing reconfigurable photoresponsivity is often blocked by the requirement for high programming voltages and the limits of the detection spectrum range. This greatly restricts the progress of energy-efficient and precise neuromorphic vision sensing. Herein, a reconfigurable photodetector with low programming voltage and broadband response is presented via in situ intercalation of Cu<sup>+</sup> ions into the van der Waals (vdW) gaps of thermoelectric 2D material SnS<sub>2</sub>. Interestingly, the vdW gaps provide an ionic transport channel with lower energy barriers compared to oxide-based memristors, resulting in a low programming voltage (0.5 V). Furthermore, reversible conversion of photo-detection is achieved from photovoltaic to photo-thermoelectric (PTE) mode via voltage-controlled ion distribution, which modulates the phonon scattering rate in the neighboring SnS<sub>2</sub> layer. As a result, the response spectrum switches from visible (532 nm) to long-wave infrared (10 µm) with an on/off ratio as high as 10<sup>4</sup>. Thus, dual-mode conversion and broadband detection functionality in reconfigurable imaging are realized, suggesting a potential pathway for the development of highly energy-efficient reconfigurable optoelectronics with a spectrum far beyond human vision.

SmallVol. 21(6)
Nanchang University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 36%
Advanced Memory and Neural Computing
0.42
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