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Xu Y, Xu X, Huang Y, Tian Y, Cheng M, Deng J, Xie Y, Zhang Y, Zhang P, Wang X, Wang Z, Li M, Li L, Liu M. Gate-Tunable Positive and Negative Photoconductance in Near-Infrared Organic Heterostructures for In-Sensor Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2402903. [PMID: 38710094 DOI: 10.1002/adma.202402903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/23/2024] [Indexed: 05/08/2024]
Abstract
The 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.
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Affiliation(s)
- Yunqi Xu
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaolu Xu
- Global Health Drug Discovery Institute, Beijing, 100192, China
| | - Ying Huang
- 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
| | - Ye Tian
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Miao Cheng
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Junyang Deng
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Yifan Xie
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Yanqin Zhang
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Panpan Zhang
- 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
| | - Xinhua Wang
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Zhongrui Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 999077, China
| | - Mengmeng Li
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ling Li
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Ming Liu
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
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Liu J, Wang Y, Liu Y, Wu Y, Bian B, Shang J, Li R. Recent Progress in Wearable Near-Sensor and In-Sensor Intelligent Perception Systems. SENSORS (BASEL, SWITZERLAND) 2024; 24:2180. [PMID: 38610389 PMCID: PMC11014300 DOI: 10.3390/s24072180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 03/25/2024] [Accepted: 03/27/2024] [Indexed: 04/14/2024]
Abstract
As the Internet of Things (IoT) becomes more widespread, wearable smart systems will begin to be used in a variety of applications in people's daily lives, not only requiring the devices to have excellent flexibility and biocompatibility, but also taking into account redundant data and communication delays due to the use of a large number of sensors. Fortunately, the emerging paradigms of near-sensor and in-sensor computing, together with the proposal of flexible neuromorphic devices, provides a viable solution for the application of intelligent low-power wearable devices. Therefore, wearable smart systems based on new computing paradigms are of great research value. This review discusses the research status of a flexible five-sense sensing system based on near-sensor and in-sensor architectures, considering material design, structural design and circuit design. Furthermore, we summarize challenging problems that need to be solved and provide an outlook on the potential applications of intelligent wearable devices.
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Affiliation(s)
- Jialin Liu
- CAS Key Laboratory of Magnetic Materials and Devices, Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, China Academy of Sciences, Ningbo 315201, China; (J.L.); (Y.W.); (Y.L.); (Y.W.); (B.B.)
- College of Materials Science and Opto-Electronic Technology, University of China Academy of Sciences, Beijing 100049, China
| | - Yitao Wang
- CAS Key Laboratory of Magnetic Materials and Devices, Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, China Academy of Sciences, Ningbo 315201, China; (J.L.); (Y.W.); (Y.L.); (Y.W.); (B.B.)
| | - Yiwei Liu
- CAS Key Laboratory of Magnetic Materials and Devices, Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, China Academy of Sciences, Ningbo 315201, China; (J.L.); (Y.W.); (Y.L.); (Y.W.); (B.B.)
- College of Materials Science and Opto-Electronic Technology, University of China Academy of Sciences, Beijing 100049, China
| | - Yuanzhao Wu
- CAS Key Laboratory of Magnetic Materials and Devices, Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, China Academy of Sciences, Ningbo 315201, China; (J.L.); (Y.W.); (Y.L.); (Y.W.); (B.B.)
- College of Materials Science and Opto-Electronic Technology, University of China Academy of Sciences, Beijing 100049, China
| | - Baoru Bian
- CAS Key Laboratory of Magnetic Materials and Devices, Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, China Academy of Sciences, Ningbo 315201, China; (J.L.); (Y.W.); (Y.L.); (Y.W.); (B.B.)
- College of Materials Science and Opto-Electronic Technology, University of China Academy of Sciences, Beijing 100049, China
| | - Jie Shang
- CAS Key Laboratory of Magnetic Materials and Devices, Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, China Academy of Sciences, Ningbo 315201, China; (J.L.); (Y.W.); (Y.L.); (Y.W.); (B.B.)
- College of Materials Science and Opto-Electronic Technology, University of China Academy of Sciences, Beijing 100049, China
- Materials and Optoelectronics Research Center, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Runwei Li
- CAS Key Laboratory of Magnetic Materials and Devices, Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, China Academy of Sciences, Ningbo 315201, China; (J.L.); (Y.W.); (Y.L.); (Y.W.); (B.B.)
- College of Materials Science and Opto-Electronic Technology, University of China Academy of Sciences, Beijing 100049, China
- Materials and Optoelectronics Research Center, University of Chinese Academy of Sciences, Beijing 100049, China
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Fu J, Nie C, Sun F, Li G, Shi H, Wei X. Bionic visual-audio photodetectors with in-sensor perception and preprocessing. SCIENCE ADVANCES 2024; 10:eadk8199. [PMID: 38363832 PMCID: PMC10871537 DOI: 10.1126/sciadv.adk8199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 01/17/2024] [Indexed: 02/18/2024]
Abstract
Serving as the "eyes" and "ears" of the Internet of Things, optical and acoustic sensors are the fundamental components in hardware systems. Nowadays, mainstream hardware systems, often comprising numerous discrete sensors, conversion modules, and processing units, tend to result in complex architectures that are less efficient compared to human sensory pathways. Here, a visual-audio photodetector inspired by the human perception system is proposed to enable all-in-one visual and acoustic signal detection with computing capability. This device not only captures light but also optically records sound waves, thus achieving "watching" and "listening" within a single unit. The gate-tunable positive, negative, and zero photoresponses lead to highly programmable responsivities. This programmability enables the execution of diverse functions, including visual feature extraction, object classification, and sound wave manipulation. These results showcase the potential of expanding perception approaches in neuromorphic devices, opening up new possibilities to craft intelligent and compact hardware systems.
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Affiliation(s)
- Jintao Fu
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Changbin Nie
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Feiying Sun
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
| | - Genglin Li
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haofei Shi
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, China
| | - Xingzhan Wei
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, China
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Choi C, Lee GJ, Chang S, Song YM, Kim DH. Nanomaterial-Based Artificial Vision Systems: From Bioinspired Electronic Eyes to In-Sensor Processing Devices. ACS NANO 2024; 18:1241-1256. [PMID: 38166167 DOI: 10.1021/acsnano.3c10181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2024]
Abstract
High-performance robotic vision empowers mobile and humanoid robots to detect and identify their surrounding objects efficiently, which enables them to cooperate with humans and assist human activities. For error-free execution of these robots' tasks, efficient imaging and data processing capabilities are essential, even under diverse and complex environments. However, conventional technologies fall short of meeting the high-standard requirements of robotic vision under such circumstances. Here, we discuss recent progress in artificial vision systems with high-performance imaging and data processing capabilities enabled by distinctive electrical, optical, and mechanical characteristics of nanomaterials surpassing the limitations of traditional silicon technologies. In particular, we focus on nanomaterial-based electronic eyes and in-sensor processing devices inspired by biological eyes and animal visual recognition systems, respectively. We provide perspectives on key nanomaterials, device components, and their functionalities, as well as explain the remaining challenges and future prospects of the artificial vision systems.
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Affiliation(s)
- Changsoon Choi
- Center for Optoelectronic Materials and Devices, Post-silicon Semiconductor Institute, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
| | - Gil Ju Lee
- Department of Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea
| | - Sehui Chang
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - Young Min Song
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
- AI Graduate School, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
- Department of Semiconductor Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - Dae-Hyeong Kim
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, Republic of Korea
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
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Zhang GX, Zhang ZC, Chen XD, Kang L, Li Y, Wang FD, Shi L, Shi K, Liu ZB, Tian JG, Lu TB, Zhang J. Broadband sensory networks with locally stored responsivities for neuromorphic machine vision. SCIENCE ADVANCES 2023; 9:eadi5104. [PMID: 37713483 PMCID: PMC10881039 DOI: 10.1126/sciadv.adi5104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 08/14/2023] [Indexed: 09/17/2023]
Abstract
As the most promising candidates for the implementation of in-sensor computing, retinomorphic vision sensors can constitute built-in neural networks and directly implement multiply-and-accumulation operations using responsivities as the weights. However, existing retinomorphic vision sensors mainly use a sustained gate bias to maintain the responsivity due to its volatile nature. Here, we propose an ion-induced localized-field strategy to develop retinomorphic vision sensors with nonvolatile tunable responsivity in both positive and negative regimes and construct a broadband and reconfigurable sensory network with locally stored weights to implement in-sensor convolutional processing in spectral range of 400 to 1800 nanometers. In addition to in-sensor computing, this retinomorphic device can implement in-memory computing benefiting from the nonvolatile tunable conductance, and a complete neuromorphic visual system involving front-end in-sensor computing and back-end in-memory computing architectures has been constructed, executing supervised and unsupervised learning tasks as demonstrations. This work paves the way for the development of high-speed and low-power neuromorphic machine vision for time-critical and data-intensive applications.
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Affiliation(s)
- Guo-Xin Zhang
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Zhi-Cheng Zhang
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Xu-Dong Chen
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Lixing Kang
- Key Laboratory of Multifunctional Nanomaterials and Smart Systems Division of Advanced Material, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
| | - Yuan Li
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Fu-Dong Wang
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Lei Shi
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Ke Shi
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Zhi-Bo Liu
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Jian-Guo Tian
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Tong-Bu Lu
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Jin Zhang
- Center for Nanochemistry, Beijing Science and Engineering Center for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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