Infrared In‐Sensor Computing Based on Flexible Photothermoelectric Tellurium Nanomesh Arrays
The inherent limitations of traditional von Neumann architectures hinder the rapid development of internet of things technologies. Beyond conventional, complementary metal-oxide-semiconductor technologies, imaging sensors integrated with near- or in-sensor computing architectures emerge as a promising solution. In this study, the multi-scale van der Waals (vdWs) interactions in 1D tellurium (Te) atomic chains are explored, leading to the deposition of a photothermoelectric (PTE) Te nanomesh on a polymeric polyimide substrate. The self-welding process enables the lateral vapor growth of a well-connected Te nanomesh with robust electrical and mechanical properties, including a PTE responsivity of ≈120 V W<sup>-1</sup> in the infrared light regime. Leveraging the PTE operation, the thermal-coupled bi-directional photoresponse is investigated to demonstrate a proof-of-principle in-sensor convolutional network for edge computing. This work presents a scalable approach for assembling functional vdWs Te nanomesh and highlights its potential applications in PTE image sensing and convolutional processing.
Authors
- You Meng (ORCID: https://orcid.org/0000-0002-5385-4080)
- Johnny Chung Yin Ho (ORCID: https://orcid.org/0000-0003-3000-8794)
- Changyong Lan (ORCID: https://orcid.org/0000-0002-5654-1098)
- Jiachi Liao
- Pengshan Xie (ORCID: https://orcid.org/0000-0002-5949-3458)
- Quan Quan (ORCID: https://orcid.org/0000-0001-8216-8998)
- Boxiang Gao (ORCID: https://orcid.org/0000-0001-8422-5941)
- Dong Chen (ORCID: https://orcid.org/0000-0002-2133-4697)
- Yuxuan Zhang (ORCID: https://orcid.org/0000-0002-5240-6606)
- Yan Yan (ORCID: https://orcid.org/0000-0001-5404-5026)
- Ji Zeng
- He Shao
Institutions
- Kyushu University (JP)
- University of Electronic Science and Technology of China (CN)
- Hunan University (CN)
- City University of Hong Kong (HK)
- Changsha University (CN)
- National Engineering Research Center of Electromagnetic Radiation Control Materials (CN)
Publication Details
- Journal
- Advanced Materials
- Published
- 2025-03-04
- DOI
- https://doi.org/10.1002/adma.202419653
- Citations
- 18
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 13.15
Funders
- City University of Hong Kong
- National Natural Science Foundation of China