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README.rst

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SPINS-B

SPINS-B is the open source version of SPINS, a framework for gradient-based (adjoint) photonic optimization developed over the past decade at Jelena Vuckovic's Nanoscale and Quantum Photonics Lab at Stanford University. For commercial use, the full version can be licensed through the Stanford Office of Technology and Licensing (see FAQ).

The overall architecture is explained in our paper Nanophotonic Inverse Design with SPINS: Software Architecture and Practical Considerations.

Documentation

spins-b.readthedocs.io documentation in progress.

Features

  • Gradient-based (adjoint) optimization of photonic devices
  • 2D and 3D device optimization using finite-difference frequency-domain (FDFD)
  • Support for custom objective functions, sources, and optimization methods
  • Automatically save design methodology and all hyperparameters used in optimization for reproducibility

Overview

Traditional nanophotonic design typically relies on parameter sweeps, which are expensive both in terms of computation power and time, and restrictive in their parameter space. Likewise, completely blackbox optimization algorithms, such as particle swarm and genetic algorithms, are also highly inefficient. In both these cases, the computational costs limit the degrees of the freedom of the design to be quite small. In contrast, by leveraging gradient-based optimization methods, our nanophotonic inverse design algorithms can efficiently optimize structures with tens of thousands of degrees of freedom. This enables the algorithms to explore a much larger space of structures and therefore design devices with higher efficiencies, smaller footprint, and novel functionalities.

Requirements

  • Python 3.5+
  • Some version of BLAS (e.g. OpenBLAS, ATLAS, Intel MKL)
  • Maxwell solver for 3D simulations

Recommendations

  • We recommend using virtual environments to isolate installation from the rest of the system.
  • If using OpenBLAS, we recommend setting the number of OpenBLAS threads (OPENBLAS_NUM_THREADS flag) to 1 as SPINS-B leverages parallelism itself.

Installation

Simply clone the SPINS-B repository and run pip:

$ pip3 install ./spins-b

Getting Started

See the grating coupler optimization example and the wavelength demultiplexer example in the examples folder. The grating coupler example covers setting up, running, and resuming a 2D optimization. The wavelength demultiplexer example covers setting up and running a 3D optimization as well as various ways of processing the optimization logs.

More documentation is forthcoming.

General Concepts

  • Optimization plan: The optimization plan defines all the photonic optimization problem (i.e. simulation region and desired objective) as well as the sequence of optimization steps to achieve that objective. You define an optimization plan which is then executed by SPINS-B. Doing so enables you to have an exact record of all the parameters used to design a device as well as the ability to resume optimization if the optimization fails midway.
  • Simulation space: The simulation space defines the simulation region as well as the design region (see below).
  • Design area and design region: The design region is the region of the permittivity distribution that is allowed to vary during the optimization. The design region is defined as the difference between two permittivity distributions: Where the difference is non-zero corresponds to the design region. Since most photonic devices are fabricated using top-down lithography, SPINS-B by default (this can be changed) assumes that the permittivity distribution along the z-axis is the same, and hence we speak of a design area.
  • Parametrization: The parametrization defines how to describe the permittivity of the design area. The simplest parametrization is to simply describe the value of each pixel on the Yee grid.
  • Monitors: Monitors are used to log data during the optimization process. Simple monitors simply record the value of a function whereas field monitors post-processes vector field data and can select out a particular plane to save data.
  • Transformation: Optimization in SPINS-B actually consists of a sequence of optimization problems. Each optimization is described by a transformation (because they transform the parametrization from one to another).

FAQ

What's different between SPINS-B and SPINS?

SPINS is a fully-featured optimization design suite available for commercial use. It is a superset of SPINS-B and includes the ability to design devices without writing any code with user-friendly interfaces and to apply precise fabrication constraints (minimum gap and curvature constraints). All devices shown in our published work rely on capabilities found in the fully-featured SPINS.

How are structures simulated?

SPINS-B uses the finite difference frequency domain (FDFD) simulation method. This choice was made because in many photonic device designs, we are concerned with device operation in a small bandwidth at particular frequencies. The FDFD method is often faster than the more widely used finite difference time domain (FDTD) method in these cases.

SPINS-B can use both a CPU-based solver or the GPU-accelerated Maxwell FDFD solver. For 2D simulations, we recommend using a direct matrix CPU-based solver ("local_direct") because it is faster. 3D simulations require too much memory and an iterative solver must be used. We recommend the GPU-accelerated MaxwellFDFD solver ("maxwell_cg") in this case.

Publications

Any publications resulting from the use of this software should acknowledge SPINS-B and cite the following papers:

For general device optimization:

  • Su et al. Nanophotonic Inverse Design with SPINS: Software Architecture and Practical Considerations. arXiv:1910.04829 (2019).

For grating coupler optimization:

  • Su et al. Fully-automated optimization of grating couplers. Opt. Express (2018).
  • Sapra et al. Inverse design and demonstration of broadband grating couplers. IEEE J. Sel. Quant. Elec. (2019).
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