Ansari Samaneh, Bianconi Simone, Kang Chang-Mo, Mohseni Hooman
Electrical and Computer Engneering Department, Northwestern University, Evanston, IL, 60208, USA.
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA.
Small Methods. 2024 Feb;8(2):e2300595. doi: 10.1002/smtd.202300595. Epub 2023 Jul 27.
The last two decades have witnessed a dramatic increase in research on low-dimensional material with exceptional optoelectronic properties. While low-dimensional materials offer exciting new opportunities for imaging, their integration in practical applications has been slow. In fact, most existing reports are based on single-pixel devices that cannot rival the quantity and quality of information provided by massively parallelized mega-pixel imagers based on complementary metal-oxide semiconductor (CMOS) readout electronics. The first goal of this review is to present new opportunities in producing high-resolution cameras using these new materials. New photodetection methods and materials in the field are presented, and the challenges involved in their integration on CMOS chips for making high-resolution cameras are discussed. Practical approaches are then presented to address these challenges and methods to integrate low-dimensional material on CMOS. It is also shown that such integrations could be used for ultra-low noise and massively parallel testing of new material and devices. The second goal of this review is to present the colossal untapped potential of low-dimensional material in enabling the next-generation of low-cost and high-performance cameras. It is proposed that low-dimensional materials have the natural ability to create excellent bio-inspired artificial imaging systems with unique features such as in-pixel computing, multi-band imaging, and curved retinas.
在过去二十年中，对具有卓越光电特性的低维材料的研究急剧增加。虽然低维材料为成像提供了令人兴奋的新机遇，但其在实际应用中的整合进展缓慢。事实上，大多数现有报告基于单像素器件，这些器件无法与基于互补金属氧化物半导体（CMOS）读出电子学的大规模并行百万像素成像器所提供的信息数量和质量相媲美。本综述的首要目标是介绍使用这些新材料制造高分辨率相机的新机遇。文中介绍了该领域的新光电探测方法和材料，并讨论了将它们集成到CMOS芯片上以制造高分辨率相机所涉及的挑战。随后提出了应对这些挑战的实际方法以及将低维材料集成到CMOS上的方法。还表明，这种集成可用于对新材料和器件进行超低噪声和大规模并行测试。本综述的第二个目标是展示低维材料在实现下一代低成本、高性能相机方面巨大的未开发潜力。有人提出，低维材料具有天然能力，可创建具有像素内计算、多波段成像和弯曲视网膜等独特功能的出色仿生人工成像系统。
