Light harvesting and carrier separation in N-doped graphene clusters/Si heterojunctions for photodetection and spatial image recognition

G Genqiang Cao (School of Physical Science and Technology, Ningbo University 1 , Ningbo 315211,) H Hui Ma (Key Laboratory of Sustainable Low-carbon Technologies for Textile Dyeing and Finishing, Ministry of Education, State Key Laboratory of Advanced Fiber Materials, College of Chemistry and Chemical Engineering) F Fanghao Zhu (School of Physical Science and Technology, Ningbo University 1 , Ningbo 315211,) K Kuan Qian (School of Physical Science and Technology, Ningbo University 1 , Ningbo 315211,) S Shubo Li (School of Physical Science and Technology, Ningbo University 1 , Ningbo 315211,) S Shixia Luan (School of Electronic and Information Engineering, Ningbo University of Technology 2 , Ningbo 315211,) W Wenwu Xu X Xu Wang Q Qinglei Guo (School of Integrated Circuits, Shandong University 3 , Jinan 250100,) G Gang Wang

Abstract

Two-dimensional (2D) graphene photodetectors have limited light absorption, short carrier lifetimes, and a dependence on external bias, which hinder sensitivity and power efficiency. This study presents a three-dimensional (3D) heterojunction of nitrogen (N)-doped graphene clusters, featuring 82% porosity, constructed on a silicon (Si) substrate. The 3D structure enhances light–matter interaction and trapping through localized optical field confinement in concave areas. Scanning kelvin probe microscopy and conductive atomic force microscopy results reveal that N doping enhances band alignment, boosts the built-in electric field, and improves carrier transport. The device enables broadband self-powered detection from 440 to 1550 nm without bias. At 1550 nm, it has a responsivity of 24.5 A/W and specific detectivity of 1.2 × 1012 Jones (under −3 V bias and 40 mW/cm2), and a low noise power spectral density of 3 × 10−26 A2/Hz. It features a −3 dB bandwidth of 1.5 kHz and rise/fall times of 173/189 μs, maintaining stable performance for 4 months. By combining spatial photocurrent mapping with a deep learning model, it achieves over 97% recognition accuracy for various images. This work presents a strategy for graphene optoelectronic devices featuring broadband, high-sensitivity, self-powered, and intelligent sensing.

Article Details

Volume / Issue Vol. 129, Issue 4
Published July 27, 2026
ISSN 0003-6951
Publisher American Institute of Physics

Journal Info

Applied Physics Letters

American Institute of Physics

ISSN: 0003-6951 Physical Sciences

Authors (10)

G

Genqiang Cao

School of Physical Science and Technology, Ningbo University 1 , Ningbo 315211,

H

Hui Ma

Key Laboratory of Sustainable Low-carbon Technologies for Textile Dyeing and Finishing, Ministry of Education, State Key Laboratory of Advanced Fiber Materials, College of Chemistry and Chemical Engineering

F

Fanghao Zhu

School of Physical Science and Technology, Ningbo University 1 , Ningbo 315211,

K

Kuan Qian

School of Physical Science and Technology, Ningbo University 1 , Ningbo 315211,

S

Shubo Li

School of Physical Science and Technology, Ningbo University 1 , Ningbo 315211,

S

Shixia Luan

School of Electronic and Information Engineering, Ningbo University of Technology 2 , Ningbo 315211,

W

Wenwu Xu

X

Xu Wang

Q

Qinglei Guo

School of Integrated Circuits, Shandong University 3 , Jinan 250100,

G

Gang Wang