2D Materials‐Based Photodetectors with Bi‐Directional Responses in Enabling Intelligent Optical Sensing
Advanced Functional Materials(2025)
Abstract
AbstractWith the rapid advancement of 2D material‐based optoelectronic devices, significant progress is made in the development of all‐optical logic devices, synaptic biomimetic devices, and multidimensional detection systems. As entering to the high‐speed information era, there is an urgent demand for complex, compact, multifunctional, low‐energy, and high‐speed intelligent sensing chips. Examining the evolution of current technologies reveals a parallel in the advancement of bipolar response mechanisms‐from simple positive and negative responses to more intricate inhibition‐promotion dynamics with persistent characteristics. This evolution significantly broadens their applications in biomimetic devices. Moreover, compared to unipolar responses, complex bipolar responses offer greater flexibility in adaptation and a unique one‐to‐one mapping with high‐dimensional information parameters such as polarization, phase, and spectrum, positioning them as promising candidates for breakthroughs in multidimensional detection and resolution. In this review, design strategies are comprehensively explored for various bipolar responses in 2D materials, highlighting their deep applications and progress in advanced fields. It is aimed for this review to provide a broad overview of bi‐directional response mechanisms, offering inspiration for designing the next generation of intelligent sensing chips.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined