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Incubating advances in integrated photonics with emerging sensing and computational capabilities
Applied Physics Reviews ( IF 11.9 ) Pub Date : 2025-03-21 , DOI: 10.1063/5.0218564
Sourabh Jain 1, 2, 3 , May H. Hlaing 4 , Kang-Chieh Fan 1, 2 , Jason Midkiff 4 , Shupeng Ning 1, 2 , Chenghao Feng 1, 2 , Po-Yu Hsiao 1, 2 , Patrick T. Camp 1, 2 , Ray T. Chen 1, 2, 4
Applied Physics Reviews ( IF 11.9 ) Pub Date : 2025-03-21 , DOI: 10.1063/5.0218564
Sourabh Jain 1, 2, 3 , May H. Hlaing 4 , Kang-Chieh Fan 1, 2 , Jason Midkiff 4 , Shupeng Ning 1, 2 , Chenghao Feng 1, 2 , Po-Yu Hsiao 1, 2 , Patrick T. Camp 1, 2 , Ray T. Chen 1, 2, 4
Affiliation
As photonic technologies grow in multidimensional aspects, integrated photonics holds a unique position and continuously presents enormous possibilities for research communities. Applications include data centers, environmental monitoring, medical diagnosis, and highly compact communication components, with further possibilities continuously growing. Herein, we review state-of-the-art integrated photonic on-chip sensors that operate in the visible to mid-infrared wavelength region on various material platforms. Among the different materials, architectures, and technologies leading the way for on-chip sensors, we discuss the optical sensing principles that are commonly applied to biochemical and gas sensing. Our focus is on passive optical waveguides, including dispersion-engineered metamaterial-based structures, which are essential for enhancing the interaction between light and analytes in chip-scale sensors. We harness a diverse array of cutting-edge sensing technologies, heralding a revolutionary on-chip sensing paradigm. Our arsenal includes refractive-index-based sensing, plasmonics, and spectroscopy, which forge an unparalleled foundation for innovation and precision. Furthermore, we include a brief discussion of recent trends and computational concepts, incorporating Artificial Intelligence & Machine Learning (AI/ML) and deep learning approaches over the past few years to improve the qualitative and quantitative analysis of sensor measurements.
中文翻译:
将集成光子学的进步与新兴的传感和计算能力相结合
随着光子技术在多维方面的发展,集成光子学具有独特的地位,并不断为研究界提供巨大的可能性。应用包括数据中心、环境监测、医疗诊断和高度紧凑的通信组件,并且进一步的可能性不断增长。在本文中,我们回顾了在各种材料平台上工作在可见光到中红外波长区域的最新集成光子片上传感器。在引领片上传感器发展的不同材料、架构和技术中,我们讨论了通常用于生化和气体传感的光学传感原理。我们专注于无源光波导,包括基于色散工程的超材料结构,这对于增强芯片级传感器中光与分析物之间的相互作用至关重要。我们利用各种尖端传感技术,预示着革命性的片上传感范式。我们的武器库包括基于折光率的传感、等离子体和光谱学,它们为创新和精度奠定了无与伦比的基础。此外,我们还简要讨论了最近的趋势和计算概念,并结合了人工智能和机器学习(AI/ML)以及过去几年的深度学习方法,以提高传感器测量的定性和定量分析。
更新日期:2025-03-21
中文翻译:

将集成光子学的进步与新兴的传感和计算能力相结合
随着光子技术在多维方面的发展,集成光子学具有独特的地位,并不断为研究界提供巨大的可能性。应用包括数据中心、环境监测、医疗诊断和高度紧凑的通信组件,并且进一步的可能性不断增长。在本文中,我们回顾了在各种材料平台上工作在可见光到中红外波长区域的最新集成光子片上传感器。在引领片上传感器发展的不同材料、架构和技术中,我们讨论了通常用于生化和气体传感的光学传感原理。我们专注于无源光波导,包括基于色散工程的超材料结构,这对于增强芯片级传感器中光与分析物之间的相互作用至关重要。我们利用各种尖端传感技术,预示着革命性的片上传感范式。我们的武器库包括基于折光率的传感、等离子体和光谱学,它们为创新和精度奠定了无与伦比的基础。此外,我们还简要讨论了最近的趋势和计算概念,并结合了人工智能和机器学习(AI/ML)以及过去几年的深度学习方法,以提高传感器测量的定性和定量分析。