Boosting the Self-Driven Properties of 2D Photodetectors through Synergistic Asymmetrical Effects

Self-driven photodetectors (SDPDs) convert photon energy into electrical signals without an external bias, making them ideal for low-power optoelectronic systems. Two-dimensional materials (2DMs) offer promising platforms due to their unique properties. However, current SDPD designs face challenges: the lack of stable doping methods and the complicated 2DMs multilayer stacking techniques pose tremendous difficulties for 2DMs to adopt the same device structures (i.e., PN junctions) as bulk materials, while multiphysical field coupling approaches rely heavily on specific material properties, limiting generalizability, and the resultant self-driven performance remains at a low level. This work reveals how different asymmetries can be combined to boost self-driven properties based on typical 2D metal-semiconductor-metal PDs without complex fabrication methods. Using WSe<sub>2</sub> as the model channel material, the synergistic effect of asymmetrical contact electrodes and contact geometries is systematically demonstrated. The open-circuit voltage (<i>V</i><sub>oc</sub>) of the synergistic SDPD reaches 0.58 V, increasing by 65% and 241%, respectively, compared to the two single asymmetrical effects. Additionally, our device exhibits great sensitivity for attenuated underwater visible light signals, enabled by its high zero-bias responsivity (5.77 A/W). The proposed method offers a universal strategy to build 2DMs-SDPDs for ultralow-power consumption optoelectronics based on multiple asymmetrical effects.

Authors

Institutions

Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2025-08-18
DOI
https://doi.org/10.1021/acsami.5c10990
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Boosting the Self-Driven Properties of 2D Photodetectors through Synergistic Asymmetrical Effects

Wen Lei, Jiefei Zhu, Yanyan Luo, Changjian Zhou et al.
1 citations
ACS Applied Materials & Interfaces
Advanced Memory and Neural Computing
0.73
article

Boosting the Self-Driven Properties of 2D Photodetectors through Synergistic Asymmetrical Effects

Wen Lei, Jiefei Zhu, Yanyan Luo, Changjian Zhou, Min Zhang, Jiwei Chen, Yihong Sun, Cary Y. Yang, Yueyi Sun
article en
1 citations

Abstract

Self-driven photodetectors (SDPDs) convert photon energy into electrical signals without an external bias, making them ideal for low-power optoelectronic systems. Two-dimensional materials (2DMs) offer promising platforms due to their unique properties. However, current SDPD designs face challenges: the lack of stable doping methods and the complicated 2DMs multilayer stacking techniques pose tremendous difficulties for 2DMs to adopt the same device structures (i.e., PN junctions) as bulk materials, while multiphysical field coupling approaches rely heavily on specific material properties, limiting generalizability, and the resultant self-driven performance remains at a low level. This work reveals how different asymmetries can be combined to boost self-driven properties based on typical 2D metal-semiconductor-metal PDs without complex fabrication methods. Using WSe<sub>2</sub> as the model channel material, the synergistic effect of asymmetrical contact electrodes and contact geometries is systematically demonstrated. The open-circuit voltage (<i>V</i><sub>oc</sub>) of the synergistic SDPD reaches 0.58 V, increasing by 65% and 241%, respectively, compared to the two single asymmetrical effects. Additionally, our device exhibits great sensitivity for attenuated underwater visible light signals, enabled by its high zero-bias responsivity (5.77 A/W). The proposed method offers a universal strategy to build 2DMs-SDPDs for ultralow-power consumption optoelectronics based on multiple asymmetrical effects.

ACS Applied Materials & InterfacesVol. 17(34)
Santa Clara University (US), Chinese University of Hong Kong (HK), The University of Western Australia (AU), Peking University (CN), Chinese University of Hong Kong, Shenzhen (CN), South China University of Technology (CN)
Openalex Percentile: Top 25%
Advanced Memory and Neural Computing
0.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.