Photonic inverse design concerns the problem of finding photonic structures with target optical properties. However, traditional methods based on optimization algorithms are time-consuming and computationally expensive. Recently, deep learning-based approaches have been developed to tackle the problem of inverse design efficiently. Although most of these neural network models have demonstrated high accuracy in different inverse design problems, no previous study has examined the potential effects under given constraints in nanomanufacturing. Additionally, the relative strength of different deep learning-based inverse design approaches has not been fully investigated. Here, we benchmark three commonly used deep learning models in inverse design: Tandem networks, Variational Auto-Encoders, and Generative Adversarial Networks. We provide detailed comparisons in terms of their accuracy, diversity, and robustness. We find that tandem networks and Variational Auto-Encoders give the best accuracy, while Generative Adversarial Networks lead to the most diverse predictions. Our findings could serve as a guideline for researchers to select the model that can best suit their design criteria and fabrication considerations. In addition, our code and data are publicly available, which could be used for future inverse design model development and benchmarking.
Transparent conductors are essential for high-performance optoelectronic devices. Recently, ultrathin metal films have received great attention as emerging transparent conductors to replace status quo indium tin oxide (ITO) due to their excellent optoelectrical properties with mechanical flexibility. Understanding an ultrathin metal film's optoelectrical properties with respect to thickness scaling is the prerequisite for the design of high-performance metal film-based transparent conductors. This review paper aims to focus on the evolution of ultrathin metal film’s optical properties as thickness scales. Such evolution of optical properties will be associated with electrical properties by exploring various resistivity scattering models aiming to better understand a film’s intrinsic physical property at an extremely thin scale and provide a guideline for enhancing the film’s intrinsic optoelectrical properties for transparent conductor application. Next, optical design considerations to enhance transparency at visible and near-infrared range are discussed including recent reinforcement learning methods as a potential strategy for transparent conductor design. Then, mechanical flexibility of various ITO-replacement electrodes is discussed as well as the mechanism for the metal film-based transparent conductor's excellent endurance against mechanical stress. Lastly, some of the unique benefits of using a metal film transparent conductor for optoelectronic device application are discussed.
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