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1. Shaping freeform nanophotonic devices with geometric neural parameterization

   https://doi.org/10.1038/s41524-025-01752-w  

   Dai, Tianxiang; Shao, Yixuan; Mao, Chenkai; Wu, Yu; Azzouz, Sara; Zhou, You; Fan, Jonathan A (December 2025, npj Computational Materials)

   Abstract Nanophotonic freeform design has the potential to push the performance of optical components to new limits, but there remains a challenge to effectively perform optimization while reliably enforcing design and manufacturing constraints. We present Neuroshaper, a framework for freeform geometric parameterization in which nanophotonic device layouts are defined using an analytic neural network representation. Neuroshaper serves as a qualitatively new way to perform shape optimization by capturing multi-scalar, freeform geometries in an overparameterized representation scheme, enabling effective optimization in a smoothened, high dimensional geometric design space. We show that Neuroshaper can enforce constraints and topology manipulation in a manner where local constraints lead to global changes in device morphology. We further show numerically and experimentally that Neuroshaper can apply to a diversity of nanophotonic devices. The versatility and capabilities of Neuroshaper reflect the ability of neural representation to augment concepts in topological design. 

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2. Ultrafast mode-locked laser in nanophotonic lithium niobate

   https://doi.org/10.1126/science.adj5438  

   Guo, Qiushi; Gutierrez, Benjamin K.; Sekine, Ryoto; Gray, Robert M.; Williams, James A.; Ledezma, Luis; Costa, Luis; Roy, Arkadev; Zhou, Selina; Liu, Mingchen; et al (November 2023, Science)

   Mode-locked lasers (MLLs) generate ultrashort pulses with peak powers substantially exceeding their average powers. However, integrated MLLs that drive ultrafast nanophotonic circuits have remained elusive because of their typically low peak powers, lack of controllability, and challenges when integrating with nanophotonic platforms. In this work, we demonstrate an electrically pumped actively MLL in nanophotonic lithium niobate based on its hybrid integration with a III-V semiconductor optical amplifier. Our MLL generates $\sim$4.8-ps optical pulses around 1065 nm at a repetition rate of ∼10 GHz, with energies exceeding 2.6 pJ and peak powers beyond 0.5 W. The repetition rate and the carrier-envelope offset frequency of the output can be controlled in a wide range by using the driving frequency and the pump current, providing a route for fully stabilized on-chip frequency combs. 

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3. Cryogenic packaging of nanophotonic devices with a low coupling loss < 1 dB

   https://doi.org/10.1063/5.0170324  

   Zeng, Beibei; De-Eknamkul, Chawina; Assumpcao, Daniel; Renaud, Dylan; Wang, Zhuoxian; Riedel, Daniel; Ha, Jeonghoon; Robens, Carsten; Levonian, David; Lukin, Mikhail; et al (October 2023, Applied Physics Letters)

   Robust, low-loss photonic packaging of on-chip nanophotonic circuits is a key enabling technology for the deployment of integrated photonics in a variety of classical and quantum technologies including optical communications and quantum communications, sensing, and transduction. To date, no process has been established that enables permanent, broadband, and cryogenically compatible coupling with sub-dB losses from optical fibers to nanophotonic circuits. Here, we report a technique for reproducibly generating a permanently packaged interface between a tapered optical fiber and nanophotonic devices on diamond with a record-low coupling loss <1 dB per facet at near-infrared wavelengths (∼730 nm) that remains stable from 300 K to 30 mK. We further demonstrate the compatibility of this technique with etched lithium niobate on insulator waveguides. The technique lifts performance limitations imposed by scattering as light transfers between photonic devices and optical fibers, paving the way for scalable integration of photonic technologies at both room and cryogenic temperatures. 

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4. Hybrid supervised and reinforcement learning for the design and optimization of nanophotonic structures

   https://doi.org/10.1364/OE.512159  

   Yeung, Christopher; Pham, Benjamin; Zhang, Zihan; Fountaine, Katherine_T; Raman, Aaswath_P (March 2024, Optics Express)

   From higher computational efficiency to enabling the discovery of novel and complex structures, deep learning has emerged as a powerful framework for the design and optimization of nanophotonic circuits and components. However, both data-driven and exploration-based machine learning strategies have limitations in their effectiveness for nanophotonic inverse design. Supervised machine learning approaches require large quantities of training data to produce high-performance models and have difficulty generalizing beyond training data given the complexity of the design space. Unsupervised and reinforcement learning-based approaches on the other hand can have very lengthy training or optimization times associated with them. Here we demonstrate a hybrid supervised learning and reinforcement learning approach to the inverse design of nanophotonic structures and show this approach can reduce training data dependence, improve the generalizability of model predictions, and significantly shorten exploratory training times. The presented strategy thus addresses several contemporary deep learning-based challenges, while opening the door for new design methodologies that leverage multiple classes of machine learning algorithms to produce more effective and practical solutions for photonic design. 

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5. Integrated Quantum Nanophotonics with Solution‐Processed Materials

   https://doi.org/10.1002/qute.202100078  

   Chen, Yueyang; Sharp, David; Saxena, Abhi; Nguyen, Hao; Cossairt, Brandi_M; Majumdar, Arka (November 2021, Advanced Quantum Technologies)

   Abstract A key obstacle for all quantum information science and engineering platforms is their lack of scalability. The discovery of emergent quantum phenomena and their applications in active photonic quantum technologies have been dominated by work with single atoms, self‐assembled quantum dots, or single solid‐state defects. Unfortunately, scaling these systems to many quantum nodes remains a significant challenge. Solution‐processed quantum materials are uniquely positioned to address this challenge, but the quantum properties of these materials have remained generally inferior to those of solid‐state emitters or atoms. Additionally, systematic integration of solution‐processed materials with dielectric nanophotonic structures has been rare compared to other solid‐state systems. Recent progress in synthesis processes and nanophotonic engineering, however, has demonstrated promising results, including long coherence times of emitted single photons and deterministic integration of emitters with dielectric nano‐cavities. In this review article, these recent experiments using solution‐processed quantum materials and dielectric nanophotonic structures are discussed. The progress in non‐classical light state generation, exciton‐polaritonics for quantum simulation, and spin‐physics in these materials is discussed and an outlook for this emerging research field is provided. 

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