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An end-to-end all-optical in-sensor computing system is presented using photonic integrated circuits that combine sensing and computing in the optical domain. A single-chip photonic waveguide sensor and microring weight bank perform linear processing, while nonlinear activation uses an erbium-doped fiber amplifier for real-time optical-domain computing with up to 6-bit precision, achieving 94.2% accuracy across 27 liquid-mixture classes and reducing latency and energy versus conventional designs.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/optics-end-to-end-all-optical-in-sensor-computing-system-using-photonic-integrated-circuits/638514/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/optics-end-to-end-all-optical-in-sensor-computing-system-using-photonic-integrated-circuits/638514.png","ImageObject",300,407,{"name":92,"@type":93},"Tirek","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-22",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What limitations do conventional photonic sensing systems face?","Question",{"text":112,"@type":113},"They rely on sequential wavelength scanning and electronic post-processing, which increases data redundancy, inference latency, and energy consumption due to repeated optoelectronic conversion and off-chip communication.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the proposed system combine sensing and computing?",{"text":117,"@type":113},"It merges sensing and computing in the optical domain using a single-chip photonic waveguide sensor plus a microring weight bank for linear processing, with nonlinear activation implemented via an erbium-doped fiber amplifier.",{"name":119,"@type":110,"acceptedAnswer":120},"What performance improvements are reported compared with conventional photonic sensing?",{"text":121,"@type":113},"The architecture reports real-time optical-domain computing with up to 6-bit precision, 94.2% classification accuracy over 27 liquid-mixture classes, and reduces inference latency by 4.48× and energy consumption by 11.47×.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},638514,1793103596,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},5909896572756,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Science  \nAdvA nceS | ReS eARch A Rticle  \nOPTICS  \nEnd-to-end all-optical in-sensor computing system using photonic integrated circuits  \nZian Xiao1,2,3, Zhihao Ren1,2, Yangyang Zhuge1,2, Zixuan Zhang1, YanYang4,5*,  \nBowei Dong3*, Chengkuo Lee1,2*  \nPhotonic sensors play an increasingly important role in chemical analysis, but conventional systems rely on sequential wavelength scanning and extensive electronic post-processing, leading to large data redundancy, high latency, and excessive energy consumption. Here, we introduce an end-to-end all-optical in-sensor computing system based on photonic integrated circuits that merges sensing and computing in the optical domain. The system integrates a photonic waveguide sensor and a microring weight bank on a single chip for linear processing, while nonlinear activation is implemented using an erbium-doped fiber amplifier. By performing spectral compression, this system enables real-time optical-domain computing with up to 6-bit precision. The system achieves a classification accuracy of 94.2% across 27 liquid-mixture classes and concentration prediction for mixture chemicals. Compared with conventional photonic sensing systems, the proposed architecture reduces inference latency by a factor of 4.48 and energy consumption by a factor of 11.47. These results establish a compact, low-redundancy, and energyefficient paradigm for intelligent photonic sensing at the edge.  \ncopyright © 2026 the Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. no claim to original U.S.  \nGovernment Works. distributed under a creative commons Attribution noncommercial license 4.0 (cc BY-nc) .  \nINTRODUCTION  \nPhotonic sensors have become essential tools in chemical, biological, and environmental analysis, where they enable high sensitivity, broad spectral coverage, and rapid signal acquisition (1–3). Their compatibility with integrated photonic platforms has further expanded their impact, allowing compact, stable, sensitive, and energy-efficient sensing systems far beyond those of electronic counterparts (4–6). As applications require faster responses and lower power consumption, photonic sensors are expected to play an even greater role in nextgeneration intelligent sensing technologies.  \nDespite these advantages, conventional photonic sensing architectures still typically follow a sensing-storage-then compute workflow as shown in Fig. 1A (7–9) . In a standard implementation, atunable laser injects light into the photonic sensor across different wavelengths, and a photodetector (PD) records the corresponding transmitted intensities to reconstruct the spectrum for subsequent electronic analysis. Machine-learning-assisted spectral analysis has been widely adopted to enhance feature extraction from such photonic sensor outputs ( 10, 11) . However, in most existing approaches, sensing, spectral readout, and computation remain physically and functionally decoupled, such that the measured optical information must first be converted into electronic signals and then transferred to downstream processors for digital inference. This architecture increases intermediate data handling and repeated optoelectronic processing, which in turn contributes to higher power consumption and additional latency. Moreover, optical-to-electrical conversion and off-chip data communication further reduce overall system efficiency.  \n1department of electrical & computer engineering, national University of Singapore, 4 engineering drive 3, Singapore 117576, Singapore. 2national centre for Advanced integrated Photonics (ncAiP), national University of Singapore, Singapore 119077, Singapore. 3institute of Microelectronics (iMe), Agency for Science, technology and Research (A*StAR), 2 Fusionopolis Way, innovis \\#08-02, Singapore 138634, Singapore. 4Key laboratory of Fabrication technologies for integrated circuits, chinese Academy of Sciences, Beijing 100029, china. 5institute of Micro","cbCaipIGi2m1ZEqm","https://ap.wps.com/l/cbCaipIGi2m1ZEqm","pdf",3151357,14,"English","# Introduction\n## Conventional photonic sensing workflow and its limitations\n## In-sensor computing as a solution\n## Integrated photonic computing progress and remaining gap","[{\"question\":\"What limitations do conventional photonic sensing systems face?\",\"answer\":\"They rely on sequential wavelength scanning and electronic post-processing, which increases data redundancy, inference latency, and energy consumption due to repeated optoelectronic conversion and off-chip communication.\"},{\"question\":\"How does the proposed system combine sensing and computing?\",\"answer\":\"It merges sensing and computing in the optical domain using a single-chip photonic waveguide sensor plus a microring weight bank for linear processing, with nonlinear activation implemented via an erbium-doped fiber amplifier.\"},{\"question\":\"What performance improvements are reported compared with conventional photonic sensing?\",\"answer\":\"The architecture reports real-time optical-domain computing with up to 6-bit precision, 94.2% classification accuracy over 27 liquid-mixture classes, and reduces inference latency by 4.48× and energy consumption by 11.47×.\"}]","OPTICS - End-to-end all-optical in-sensor computing system using photonic integrated circuits | PDF",1792683452,35]