The article comments on a recent advance in large-scale distributed diffractive-interference hybrid photonic chiplet Taichi for artificial general intelligence applications.
Combinatorial problems, such as the Ising problem, are hard to solve with conventional electronics. Photonic systems have recently been proposed as an efficient platform to solve these problems faster and more efficiently, thus calling for the development of featured algorithms to run on photonic machines. A few recent findings, including the Photonic Recurrent Ising Sampler, a photonic machine that recurrently solves arbitrary Ising problems, will be presented in this talk, along with their experimental realizations in various platforms.
The failure of conventional electronic architectures to solve large combinatorial problems motivates the development of novel computational architectures. There has been much effort recently in developing photonic networks which can exploit fundamental properties enshrined in the wave nature of light and of its interaction with matter: high-speed, low-power, optical passivity, and parallelization. However, unleashing the true potential of photonic architectures requires the development of featured algorithms which largely exploit these fundamental properties. We here present the Photonic Recurrent Ising Sampler (PRIS), an algorithm tailored for photonic parallel networks that can sample combinatorially hard distributions of Ising problems, in a fast and efficient manner. The PRIS finds the ground state of general ising problems and is able to probe critical behaviors of universality classes and their critical exponents. In addition to the attractive features of photonic networks, the PRIS relies on intrinsic dynamic noise to find ground states more efficiently.
We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find the network needs to be trained on only a small sampling of the data in order to approximate the simulation to high precision. Once the neural network is trained, it can simulate such optical processes orders of magnitude faster than conventional simulations. Furthermore, the trained neural network can be used solve nanophotonic inverse design problems by using back-propogation - where the gradient is analytical, not numerical.
In designing solar concentrator optics there are many parameters that must be optimized in order to create a useful system, such as compactness, number of elements or interfaces, and acceptance angle, among others. Using geometric optics, tradeoffs between these parameters become inevitable. For example, a lens, trough or dish may be compact but has low tolerance of angular misalignment; angular tolerance can be improved by adding secondary and tertiary optics, but this increases complexity and reduces optical throughput; nonimaging optics such as the CPC offer wide acceptance angles from as single element, but are too long to be practical, in most applications, above low concentrations. These tradeoffs can be avoided by using angle-selective photonic materials to exploit the equivalence between angular restriction and concentration. Recently, broadband angular selectivity in optical films has been demonstrated by the Soljacic group in MIT. In this collaborative work we use this material to experimentally demonstrate two visible-spectrum optical concentrators. We demonstrate that these concentrators are thermodynamically ideal when the material properties are ideal, and describe the material improvements most essential for improving device performance, and discuss how commercial solar concentrator systems could be improved by the use of angular-selective optics
Nanophotonic techniques can enable numerous novel and exciting phenomena. However, in order to make use of these opportunities for many applications of interest (e.g. energy, or displays), one has to have the ability to implement nanophotonic structures over large scales. In this talk, I will present some of our recent theoretical and experimental progress in exploring these opportunities.
We demonstrate designs of dielectric-filled anti-reflection coated (ARC) two-dimensional (2D) metallic photonic
crystals (MPhCs) capable of omnidirectional, polarization insensitive, wavelength selective emission/absorption. Up to
26% improvement in hemispherically averaged emittance/absorptance below the cutoff wavelength is observed for
optimized hafnium oxide filled 2D tantalum (Ta) PhCs over the unfilled 2D Ta PhCs. The optimized designs possess
high hemispherically averaged emittance/absorptance of 0.86 at wavelengths below the cutoff wavelength and low
hemispherically averaged emittance/absorptance of 0.12 at wavelengths above the cutoff wavelength, which is extremely
promising for applications such as thermophotovoltaic energy conversion, solar absorption, and infrared spectroscopy.
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