Integrated photonic biosensors provide compact, highly sensitive, and label-free platforms for biochemical detection, making them attractive for on-chip and real-time sensing applications. However, their design remains challenging due to complex resonance behaviour, strong coupling effects, and the computational cost associated with repeated full-wave electromagnetic simulations. In particular, inverse design of microring resonator-based sensors requires accurate modelling of geometry-spectrum relationships while satisfying physical constraints such as resonance conditions and spectral sensitivity requirements. In this work, we propose a physics-informed graph neural network (PI-GNN) for the inverse design of a microring resonator biosensor operating in the 1550 nm band. By representing the photonic structure as a graph and embedding resonance-based physical constraints directly into the learning objective, the model captures both structural connectivity and underlying electromagnetic principles. The proposed approach enables efficient prediction of device geometries that achieve target spectral characteristics, reducing reliance on costly simulations while maintaining physical consistency and competitive design accuracy.
We propose a new optical biosensor based on a photonic crystal with a defect layer, which can determine the SARS-CoV-2 concentration in water by the defect mode shift. Two models of the dependence of the refractive index of the defect layer on the concentration of the pathogen in water were considered. The optimal parameters for the photonic crystal in our device and the optimal thickness of the defect layer were determined. It was also demonstrated that in the presence of absorption in the investigated structure it is much more advantageous to work in the reflection mode compared to the trans…
The last two decades have witnessed an exponential growth and tremendous developments in wireless technologies and systems, and their associated applications. In the recent years following 2006, there has been a great surge in interest in the newly emerging plasmonics nanotechnology because this new device technology provides tremendous synergy between electronic and photonic devices. Electronics devices are down-scalable up to the nanoscale size but have limited processor speed due to thermal and signal delay issues associated with electronic devices. On the other hand, photonic devices have …
In this paper we employ the emerging paradigm of physics-informed neural networks (PINNs) for the solution of representative inverse scattering problems in photonic metamaterials and nano-optics technologies. In particular, we successfully apply mesh-free PINNs to the difficult task of retrieving the effective permittivity parameters of a number of finite-size scattering systems that involve many interacting nanostructures as well as multi-component nanoparticles. Our methodology is fully validated by numerical simulations based on the Finite Element Method (FEM). The development of physics-in…
Deep learning in the context of nano-photonics is mostly discussed in terms of its potential for inverse design of photonic devices or nanostructures. Many of the recent works on machine-learning inverse design are highly specific, and the drawbacks of the respective approaches are often not immediately clear. In this review we want therefore to provide a critical review on the capabilities of deep learning for inverse design and the progress which has been made so far. We classify the different deep learning-based inverse design approaches at a higher level as well as by the context of their …
Nonlocality is the missing design rule for topological photonics
Institute of Materials Science, Kiel University（基尔大学材料科学研究所）；Kiel Nano, Surface and Interface Science KiNSIS, Christian Albrechts University（克里斯蒂安·阿尔布雷希特大学基尔纳米、表面与界面科学KiNSIS）
Resonance is instrumental in modern optics and photonics for novel phenomena such as cavity quantum electrodynamics and electric-field-induced transparency. While one can use numerical simulations to sweep geometric and material parameters of optical structures, these simulations usually require considerably long calculation time (spanning from several hours to several weeks) and substantial computational resources. Such requirements significantly limit their applicability in understanding and inverse designing structures with desired resonance performances. Recently, the introduction of artif…
Enhanced Photonic Chip Design via Interpretable Machine Learning Techniques
Centre for Quantum Technologies, National University of Singapore（量子技术中心，新加坡国立大学）；School of Mathematics and Physics, University of Queensland（数学与物理学院，昆士兰大学）；Australian Research Council Centre of Excellence for Engineered Quantum Systems（澳大利亚研究理事会工程量子系统卓越中心）
Gradient-Informed Bayesian and Interior Point Optimization for Efficient Inverse Design in Nanophotonics
Institute of Information Processing, Leibniz University Hannover（莱比锡洪堡大学信息处理研究所）；Flexcompute, Watertown, MA, USA（Flexcompute公司）；Institute of Artificial Intelligence, Leibniz University Hannover（莱比锡洪堡大学人工智能研究所）；L3S Research Center, Hannover, Germany（L3S研究中心）
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Massachusetts Institute of Technology（麻省理工学院）
Optical computing is an emerging technology for next-generation efficient artificial intelligence (AI) due to its ultra-high speed and efficiency. Electromagnetic field simulation is critical to the design, optimization, and validation of photonic devices and circuits. However, costly numerical simulation significantly hinders the scalability and turn-around time in the photonic circuit design loop. Recently, physics-informed neural networks have been proposed to predict the optical field solution of a single instance of a partial differential equation (PDE) with predefined parameters. Their c…
Neural network inverse design of nanophotonic scintillators
Technion – Israel Institute of Technology（以色列理工学院）；Institute for Instrumentation in Molecular Imaging (I3M), CSIC-Universitat Politècnica de València（分子成像仪器研究所，西班牙瓦伦西亚理工大学）；E.L. Ginzton Laboratory, Stanford University（斯坦福大学）；Institute of Science and Technology Austria (ISTA)（奥地利科学技术研究院）
