Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userBayesian optimization with improved scalability and derivative information for efficient design of nanophotonic structures
69
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We propose the combination of forward shape derivatives and the use of an iterative inversion scheme for Bayesian optimization to find optimal designs of nanophotonic devices. This approach widens the range of applicability of Bayesian optmization to situations where a larger number of iterations is required and where derivative information is available. This was previously impractical because the computational efforts required to identify the next evaluation point in the parameter space became much larger than the actual evaluation of the objective function. We demonstrate an implementation of the method by optimizing a waveguide edge coupler.
rate research
Read More
Numerical optimization is an important tool in the field of computational physics in general and in nano-optics in specific. It has attracted attention with the increase in complexity of structures that can be realized with nowadays nano-fabrication technologies for which a rational design is no longer feasible. Also, numerical resources are available to enable the computational photonic material design and to identify structures that meet predefined optical properties for specific applications. However, the optimization objective function is in general non-convex and its computation remains resource demanding such that the right choice for the optimization method is crucial to obtain excellent results. Here, we benchmark five global optimization methods for three typical nano-optical optimization problems: removed{downhill simplex optimization, the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm, particle swarm optimization, differential evolution, and Bayesian optimization} added{particle swarm optimization, differential evolution, and Bayesian optimization as well as multi-sta
Numerical simulation of complex optical structures enables their optimization with respect to specific objectives. Often, optimization is done by multiple successive parameter scans, which are time consuming and computationally expensive. We employ here Bayesian optimization with Gaussian processes in order to automatize and speed up the optimization process. As a toy example, we demonstrate optimization of the shape of a free-form reflective meta surface such that it diffracts light into a specific diffraction order. For this example, we compare the performance of six different Bayesian optimization approaches with various acquisition functions and various kernels of the Gaussian process.
We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predictions across multiple levels of fidelity. Through the posterior variance of the MFGP, our framework naturally enables uncertainty quantification, providing estimates of confidence in the predictions. We used Density Functional Theory as high-fidelity prediction, while a ML interatomic potential is used as the low-fidelity prediction. Practical materials design efficiency is demonstrated by reproducing the ternary composition dependence of a quantity of interest (bulk modulus) across the full aluminum-niobium-titanium ternary random alloy composition space. The MFGP is then coupled to a Bayesian optimization procedure and the computational efficiency of this approach is demonstrated by performing an on-the-fly search for the global optimum of bulk modulus in the ternary composition space. The framework presented in this manuscript is the first application of MFGP to atomistic materials simulations fusing predictions between Density Functional Theory and classical interatomic potential calculations.
We reapply our approach to designing nanophotonic quantum memories to formulate an optical network that autonomously protects a single logical qubit against arbitrary single-qubit errors. Emulating the 9 qubit Bacon-Shor subsystem code, the network replaces the traditionally discrete syndrome measurement and correction steps by continuous, time-independent optical interactions and coherent feedback of unitarily processed optical fields.
Progress in integrated nanophotonics has enabled large-scale programmable photonic integrated circuits (PICs) for general-purpose electronic-photonic systems on a chip. Relying on the weak, volatile thermo-optic or electro-optic effects, such systems usually exhibit limited reconfigurability along with high energy consumption and large footprints. These challenges can be addressed by resorting to chalcogenide phase-change materials (PCMs) such as Ge2Sb2Te5 (GST) that provide substantial optical contrast in a self-holding fashion upon phase transitions. However, current PCM-based integrated photonic applications are limited to single devices or simple PICs due to the poor scalability of the optical or electrical self-heating actuation approaches. Thermal-conduction heating via external electrical heaters, instead, allows large-scale integration and large-area switching, but fast and energy-efficient electrical control is yet to show. Here, we model electrical switching of GST-clad integrated nanophotonic structures with graphene heaters based on the programmable GST-on-silicon platform. Thanks to the ultra-low heat capacity and high in-plane thermal conductivity of graphene, the proposed structures exhibit a high switching speed of ~80 MHz and high energy efficiency of 19.2 aJ/nm^3 (6.6 aJ/nm^3) for crystallization (amorphization) while achieving complete phase transitions to ensure strong attenuation (~6.46 dB/micron) and optical phase (~0.28 dB/micron at 1550 nm) modulation. Compared with indium tin oxide and silicon p-i-n heaters, the structures with graphene heaters display two orders of magnitude higher figure of merits for heating and overall performance. Our work facilitates the analysis and understanding of the thermal-conduction heating-enabled phase transitions on PICs and supports the development of the future large-scale PCM-based electronic-photonic systems.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
