Year: 2025
Type: article
Abstract: High Resolution Image Download MS PowerPoint Slide Light detection and ranging (LiDAR) technology is critical for autonomous driving, which relies on abundant near-infrared (NIR) photodetectors to accurately capture three-dimensional spatial information. CMOS-based LiDAR detection systems are less efficient with respect to computational efficiency, inherently limited by their von Neumann architecture, leading to high latency and significant computational demands. Retina-inspired neuromorphic sensors integrated from sensing and computation present a promising alternative but need additional analog-to-digital converters (ADCs). Here we present the photosensitive ring oscillator (PRO) based visual afferent neuro-biosensors for LiDAR-based sensing applications. The PRO employs monolayer MoS 2 as channels decorated by Nd 3+ /Yb 3+ /Er 3+ tridoped NaYF 4 up-conversion nanoparticles (UCNPs) to achieve near-infrared (NIR)-triggered oscillation frequency modulation. The PRO architecture avoids the need for ADCs, offering enhanced noise immunity and system simplicity. The simulated VoxelNet neural network effectively preprocesses images by extracting environmental information, achieving high recognition accuracy especially in low-light conditions. This work presents a new paradigm for developing a real-time, high-accuracy LiDAR sensing system.
Source: ACS Nano
Authors: Xingchao Zhang, Chunsheng Chen, Lanying Zhou, Minkun Jin, Changheng Chen, Qing Guan, Shuopei Wang, Tong Li, Songge Zhang, Hua Yu, Shenghuang Lin, Na Li, Chongfeng Guo, Jianbin Xu, Guangyu Zhang
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
OpenAlex ID: W4417438437