Affiliation:
1. Key Lab of Fabrication Technologies for Integrated Circuits Institute of Microelectronics Chinese Academy of Sciences Beijing 100029 China
2. University of Chinese Academy of Sciences Beijing 100049 China
3. Global Health Drug Discovery Institute Beijing 100192 China
4. Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen 518060 China
5. State Key Laboratory of Materials Processing and Die & Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China
6. Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong 999077 China
Abstract
AbstractThe rapid growth of sensor data in the artificial intelligence often causes significant reductions in processing speed and power efficiency. Addressing this challenge, in‐sensor computing is introduced as an advanced sensor architecture that simultaneously senses, memorizes, and processes images at the sensor level. However, this is rarely reported for organic semiconductors that possess inherent flexibility and tunable bandgap. Herein, an organic heterostructure that exhibits a robust photoresponse to near‐infrared (NIR) light is introduced, making it ideal for in‐sensor computing applications. This heterostructure, consisting of partially overlapping p‐type and n‐type organic thin films, is compatible with conventional photolithography techniques, allowing for high integration density of up to 520 devices cm−2 with a 5 µm channel length. Importantly, by modulating gate voltage, both positive and negative photoresponses to NIR light (1050 nm) are attained, which establishes a linear correlation between responsivity and gate voltage and consequently enables real‐time matrix multiplication within the sensor. As a result, this organic heterostructure facilitates efficient and precise NIR in‐sensor computing, including image processing and nondestructive reading and classification, achieving a recognition accuracy of 97.06%. This work serves as a foundation for the development of reconfigurable and multifunctional NIR neuromorphic vision systems.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Cited by
1 articles.
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