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Advanced materials (Deerfield Beach, Fla.)
Jul, 2024;36 (30): e2402903.

Gate-Tunable Positive and Negative Photoconductance in Near-Infrared Organic Heterostructures for In-Sensor Computing.

Yunqi Xu, Xiaolu Xu, Ying Huang, Ye Tian, Miao Cheng, Junyang Deng, Yifan Xie, Yanqin Zhang, Panpan Zhang, Xinhua Wang, Zhongrui Wang, Mengmeng Li, Ling Li, Ming Liu
Author Information
  1. Yunqi Xu: Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
  2. Xiaolu Xu: Global Health Drug Discovery Institute, Beijing, 100192, China.
  3. 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.
  4. Ye Tian: Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
  5. Miao Cheng: Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
  6. Junyang Deng: Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
  7. Yifan Xie: Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
  8. Yanqin Zhang: Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
  9. 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.
  10. Xinhua Wang: Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
  11. Zhongrui Wang: Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 999077, China.
  12. Mengmeng Li: Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China. ORCID
  13. Ling Li: Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
  14. Ming Liu: Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
PMID: 38710094 DOI: 10.1002/adma.202402903

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 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.

Keywords

in‐sensor computing near‐infrared photoresponse organic heterostructure photogating effect photolithography

References

  1. a) T. Balch, R. C. Arkin, IEEE Trans. Rob. Autom. 1998, 14, 926;
  2. b) P. Glynne‐Jones, M. J. Tudor, S. P. Beeby, N. M. White, Sens. Actuators, A 2004, 110, 344;
  3. c) D. Gonzalez, J. Perez, V. Milanes, F. Nashashibi, IEEE Trans. Intell. Transp. Syst. 2016, 17, 1135;
  4. d) M. L. Hammock, A. Chortos, B. C. K. Tee, J. B. H. Tok, Z. A. Bao, Adv. Mater. 2013, 25, 5997;
  5. e) T. Someya, Y. Kato, T. Sekitani, S. Iba, Y. Noguchi, Y. Murase, H. Kawaguchi, T. Sakurai, Proc. Natl. Acad. Sci. USA 2005, 102, 12321;
  6. f) P. Varaiya, IEEE Trans. Autom. Control 1993, 38, 195;
  7. g) S. Wang, H. Chen, T. Liu, Y. Wei, G. Yao, Q. Lin, X. Han, C. Zhang, H. Huang, Angew. Chem., Int. Ed. Engl. 2023, 62, 202213733;
  8. h) Y. Xie, C. Ding, Q. Jin, L. Zheng, Y. Xu, H. Xiao, M. Cheng, Y. Zhang, G. Yang, M. Li, L. Li, M. Liu, Smart Mater. 2024, e1261.
  9. a) J. F. Huang, J. Lee, J. Vollbrecht, V. V. Brus, A. L. Dixon, D. X. Cao, Z. Y. Zhu, Z. F. Du, H. B. Wang, K. Cho, G. C. Bazan, T. Q. Nguyen, Adv. Mater. 2020, 32, 1906027;
  10. b) Z. T. Zhang, M. Liao, H. Q. Lou, Y. J. Hu, X. M. Sun, H. S. Peng, Adv. Mater. 2018, 30, 1704261;
  11. c) M. M. Li, C. B. An, T. Marszalek, X. Guo, Y. Z. Long, H. X. Yin, C. Z. Gu, M. Baumgarten, W. Pisula, K. Mullen, Chem. Mater. 2015, 27, 2218;
  12. d) M. Li, A. H. Balawi, P. J. Leenaers, L. Ning, G. H. L. Heintges, T. Marszalek, W. Pisula, M. M. Wienk, S. C. J. Meskers, Y. Yi, F. Laquai, R. A. J. Janssen, Nat. Commun. 2019, 10, 2867;
  13. e) M. Li, P. J. Leenaers, M. M. Wienk, R. A. J. Janssen, J. Mater. Chem. C 2020, 8, 5856.
  14. a) Y. Cai, Z. Wei, C. H. Song, C. C. Tang, W. Han, X. C. Dong, Chem. Soc. Rev. 2019, 48, 22;
  15. b) F. Ding, Y. B. Zhan, X. J. Lu, Y. Sun, Chem. Sci. 2018, 9, 4370;
  16. c) S. J. Zhu, R. Tian, A. L. Antaris, X. Y. Chen, H. J. Dai, Adv. Mater. 2019, 31, 1900321;
  17. d) Q. Y. Li, Y. L. Guo, Y. Q. Liu, Chem. Mater. 2019, 31, 6359;
  18. e) A. Pierre, A. Gaikwad, A. C. Arias, Nat. Photonics 2017, 11, 193;
  19. f) J. K. Song, J. Kim, J. Yoon, J. H. Koo, H. Jung, K. Kang, S. H. Sunwoo, S. Yoo, H. Chang, J. Jo, W. Baek, S. Lee, M. Lee, H. J. Kim, M. Shin, Y. J. Yoo, Y. M. Song, T. Hyeon, D. H. Kim, D. Son, Nat. Nanotechnol. 2022, 17, 849;
  20. g) D. Y. Zhu, D. Y. Ji, L. Q. Li, W. P. Hu, J. Mater. Chem. C 2022, 10, 13312.
  21. T. Yokota, T. Nakamura, H. Kato, M. Mochizuki, M. Tada, M. Uchida, S. Lee, M. Koizumi, W. Yukita, A. Takimoto, T. Someya, Nat. Electron. 2020, 3, 113.
  22. J. Kim, J. Kim, S. Jo, J. Kang, J. W. Jo, M. Lee, J. Moon, L. Yang, M. G. Kim, Y. H. Kim, S. K. Park, Adv. Mater. 2016, 28, 3078.
  23. a) Y. H. Chen, G. Y. Gao, J. Zhao, H. Zhang, J. R. Yu, X. X. Yang, Q. Zhang, W. L. Zhang, S. Y. Xu, J. Sun, Y. F. Meng, Q. J. Sun, Adv. Funct. Mater. 2019, 29, 1900959;
  24. b) C. S. Liu, X. Yan, X. F. Song, S. J. Ding, D. W. Zhang, P. Zhou, Nat. Nanotechnol. 2018, 13, 404;
  25. c) S. Manipatruni, D. E. Nikonov, I. A. Young, Nat. Phys. 2018, 14, 338;
  26. d) C. Wu, T. W. Kim, H. Y. Choi, D. B. Strukov, J. J. Yang, Nat. Commun. 2017, 8, 752.
  27. a) J. J. Hopfield, Proc. Natl. Acad. Sci. U SA 1982, 79, 2554;
  28. b) Y. LeCun, Y. Bengio, G. Hinton, Nature 2015, 521, 436;
  29. c) R. Midya, Z. Wang, S. Asapu, X. Zhang, M. Rao, W. Song, Y. Zhuo, N. Upadhyay, Q. Xia, J. J. Yang, Adv. Intell. Syst. 2019, 1, 1900084;
  30. d) J. Moon, W. Ma, J. H. Shin, F. Cai, C. Du, S. H. Lee, W. D. Lu, Nat. Electron. 2019, 2, 480.
  31. a) V. Sze, Y. H. Chen, J. Emer, A. Suleiman, Z. D. Zhang, presented at 38th IEEE Annual Custom Integrated Circuits Conf. (CICC), Austin, TX, April–May, 2017;
  32. b) Y. Chai, Nature 2020, 579, 32.
  33. Z. H. He, H. G. Shen, D. K. Ye, L. Y. Xiang, W. R. Zhao, J. M. Ding, F. J. Zhang, C. A. Di, D. B. Zhu, Nat. Electron. 2021, 4, 522.
  34. C. C. Hung, Y. C. Chiang, Y. C. Lin, Y. C. Chiu, W. C. Chen, Adv. Sci. 2021, 8, 2100742.
  35. a) H. Y. Wang, S. Jiang, Z. Q. Hao, X. Xu, M. J. Pei, J. H. Guo, Q. J. Wang, Y. T. Li, J. M. Chen, J. Xu, X. R. Wang, J. Z. Wang, Y. Shi, Y. Li, J. Phys. Chem. Lett. 2022, 13, 2338;
  36. b) Z. Q. Hao, H. Y. Wang, S. Jiang, J. Qian, X. Xu, Y. T. Li, M. J. Pei, B. W. Zhang, J. H. Guo, H. J. Zhao, J. M. Chen, Y. F. Tong, J. P. Wang, X. R. Wang, Y. Shi, Y. Li, Adv. Sci. 2022, 9, 2103494;
  37. c) Z. Y. Lv, M. Chen, F. S. Qian, V. A. L. Roy, W. B. Ye, D. H. She, Y. Wang, Z. X. Xu, Y. Zhou, S. T. Han, Adv. Funct. Mater. 2019, 29, 1902374;
  38. d) B. Pradhan, S. Das, J. Li, F. Chowdhury, J. Cherusseri, D. Pandey, D. Dev, A. Krishnaprasad, E. Barrios, A. Towers, A. Gesquiere, L. Tetard, T. Roy, J. Thomas, Sci. Adv. 2020, 6, eaay5225;
  39. e) J. Lao, M. G. Yan, B. B. Tian, C. L. Jiang, C. H. Luo, Z. Z. Xie, Q. X. Zhu, Z. Q. Bao, N. Zhong, X. D. Tang, L. F. Sun, G. J. Wu, J. L. Wang, H. Peng, J. H. Chu, C. G. Duan, Adv. Sci. 2022, 9, 2106092.
  40. T. Q. Wan, B. J. Shao, S. J. Ma, Y. Zhou, Q. Li, Y. Chai, Adv. Mater. 2022, 35, 2203830.
  41. a) L. Mennel, J. Symonowicz, S. Wachter, D. K. Polyushkin, A. J. Molina‐Mendoza, T. Mueller, Nature 2020, 579, 62;
  42. b) L. Pi, P. Wang, S. J. Liang, P. Luo, H. Wang, D. Li, Z. Li, P. Chen, X. Zhou, F. Miao, T. Zhai, Nat. Electron. 2022, 5, 248;
  43. c) C. Y. Wang, S. J. Liang, S. Wang, P. Wang, Z. a. Li, Z. Wang, A. Gao, C. Pan, C. Liu, J. Liu, H. Yang, X. Liu, W. Song, C. Wang, X. Wang, K. Chen, Z. Wang, K. Watanabe, T. Taniguchi, J. J. Yang, F. Miao, B. Cheng, Sci. Adv. 2020, 6, eaba6173;
  44. d) Y. R. Wang, Y. C. Cai, F. Wang, J. Yang, T. Yan, S. H. Li, Z. L. Wu, X. Y. Zhan, K. Xu, J. He, Z. X. Wang, Nano Lett. 2023, 23, 4524.
  45. F. C. Zhou, Y. Chai, Nat. Electron. 2020, 3, 664.
  46. a) M. Long, P. Wang, H. Fang, W. Hu, Adv. Funct. Mater. 2019, 29, 1803807;
  47. b) H. Fang, W. Hu, Adv. Sci. 2017, 4, 1700323.
  48. Y. Huang, Y. Xu, K. Liu, Y. Fu, A. G. Ricciardulli, F. Wang, S. Yang, J. Ma, M. Li, Z. Qian, R. Wang, P. Zhang, ACS Mater. Lett. 2024, 6, 1069.
  49. a) K. Kobashi, R. Hayakawa, T. Chikyow, Y. Wakayama, J. Phys. Chem. C 2018, 122, 6943;
  50. b) K. Kobashi, R. Hayakawa, T. Chikyow, Y. Wakayama, ACS Appl. Mater. Interfaces 2018, 10, 2762;
  51. c) S. W. Jo, J. Choi, R. Hayakawa, Y. Wakayama, S. Jung, C. H. Kim, J. Mater. Chem. C 2021, 9, 15415;
  52. d) K. Kobashi, R. Hayakawa, T. Chikyow, Y. Wakayama, Nano Lett. 2018, 18, 4355;
  53. e) K. Kobashi, R. Hayakawa, T. Chikyow, Y. Wakayama, Adv. Electron. Mater. 2017, 3, 1700106;
  54. f) Y. Wakayama, R. Hayakawa, Adv. Funct. Mater. 2020, 30, 1903724.
  55. L. A. Gatys, A. S. Ecker, M. Bethge, presented at 2016 IEEE Conf. Computer Vision Pattern Recognition (CVPR), Seattle, WA, June, 2016.

Grants

  1. 2019YFA0706100/National Key R&D Program of China
  2. 62074163/National Natural Science Foundation of China
Journal Article
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Word Cloud

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