This paper introduces SiQAD, a computer-aided design tool enabling the rapid design and simulation of atomic silicon dangling bond quantum dot patterns capable of computational logic. Several simulation tools are included, each able to inform the designer on various aspects of their designs: a ground-state electron configuration finder, a non-equilibrium electron dynamics simulator, and an electric potential landscape solver with clocking electrode support. Simulations have been compared against past experimental results to inform the electron population estimation and dynamic behavior. New logic gates suitable for this platform have been designed and simulated, and a clocked wire has been demonstrated. This work paves the way for the exploration of the vast and fertile design space of atomic silicon dangling bond quantum dot circuits.

Optimization methods are playing an increasingly important role in all facets of photonics engineering, from integrated photonics to free space diffractive optics. However, efforts in the photonics community to develop optimization algorithms remain uncoordinated, which has hindered proper benchmarking of design approaches and access to device designs based on optimization. We introduce MetaNet, an online database of photonic devices and design codes intended to promote coordination and collaboration within the photonics community.Using metagratings as a model system, we have uploaded over one hundred thousand device layouts to the database, as well as source code for implementations of local and global topology optimization methods. Further analyses of these large datasets allow the distribution of optimized devices to be visualized for a given optimization method. We expect that the coordinated research efforts enabled by MetaNet will expedite algorithm development for photonics design.

Robust automated design tools are crucial for the proliferation of any computing technology. We introduce the first automated design tool for the silicon dangling bond quantum dot computing technology, which is an extremely versatile and flexible single-atom computing circuitry framework. The automated designer is capable of navigating the complex, hyperdimensional design spaces of arbitrarily sized design areas and truth tables by employing a tabula rasa double-deep Q-learning reinforcement learning algorithm. Robust policy convergence is demonstrated for a wide range of two-input, one-output logic circuits and a two-input, two-output half-adder, designed with an order of magnitude fewer SiDBs in several orders of magnitude less time than the only other half-adder demonstrated in the literature. We anticipate that reinforcement learning-based automated design tools will accelerate the development of the SiDB quantum dot computing technology, leading to its eventual adoption in specialized computing hardware.

We introduce WaveY-Net, a hybrid data-and physics-augmented convolutional neural network that can predict electromagnetic field distributions with ultrafast speeds and high accuracy for entire classes of dielectric nanophotonic structures. This accuracy is achieved by training the neural network to learn only the magnetic near-field distributions of a system and to use a discrete formalism of Maxwell's equations in two ways: to calculate electric fields from the magnetic fields and as physical constraints in the loss function. We show that WaveY-Net can accurately predict the near-fields in periodic, high dielectric contrast nanostructure arrays, and that it can combine with gradientbased algorithms to dramatically accelerate the local and global freeform optimization of diffractive photonic devices by orders of magnitude faster speeds. We anticipate that physics-augmented deep neural networks will transform the practice of nanophotonics simulation and design.

The calculation of electromagnetic field distributions within structured media is central to the optimization and validation of photonic devices. We introduce WaveY-Net, a hybrid data-and physics-augmented convolutional neural network that can predict electromagnetic field distributions with ultra fast speeds and high accuracy for entire classes of dielectric photonic structures. This accuracy is achieved by training the neural network to learn only the magnetic near-field distributions of a system and to use a discrete formalism of Maxwell's equations in two ways: as physical constraints in the loss function and as a means to calculate the electric fields from the magnetic fields. As a model system, we construct a surrogate simulator for periodic silicon nanostructure arrays and show that the high speed simulator can be directly and effectively used in the local and global freeform optimization of metagratings. We anticipate that physics-augmented networks will serve as a viable Maxwell simulator replacement for many classes of photonic systems, transforming the way they are designed.