Usage¶

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Description¶

Output installed packages in requirements format.

packages are listed in a case-insensitive sorted order.

Options¶

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Use the order in the given requirements file and its comments when generating output. This option can be used multiple times.

URL for finding packages, which will be added to the output.

If in a virtualenv that has global access, do not output globally-installed packages.

Only output packages installed in user-site.

Restrict to the specified installation path for listing packages (can be used multiple times).

Do not skip these packages in the output: setuptools, distribute, pip, wheel

Exclude editable package from output.

Exclude specified package from the output

Examples¶

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1. Generate output suitable for a requirements file.

   Unix/macOSWindows

2. Generate a requirements file and then install from it in another environment.

   Unix/macOSWindows

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SPINS-B is the open source version of SPINS,a framework for gradient-based (adjoint) photonic optimization developed overthe past decade at Jelena Vuckovic’s Nanoscale and Quantum Photonics Labat Stanford University. For commercial use, the full version can be licensedthrough 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.

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

Upcoming Features¶

We are protoyping the next version of SPINS, known as Goos. Thisversion of SPINS will support these new features:

• Integration with FDTD solvers
• Co-optimization of multiple device regions simulataneously
• Easier to use and extend

Overview¶

Traditional nanophotonic design typically relies on parameter sweeps, which areexpensive both in terms of computation power and time, and restrictive in theirparameter space. Likewise, completely blackbox optimization algorithms, suchas particle swarm and genetic algorithms, are also highly inefficient. In boththese cases, the computational costs limit the degrees of the freedom of thedesign to be quite small. In contrast, byleveraging gradient-based optimization methods, our nanophotonic inverse designalgorithms can efficiently optimize structures with tens of thousands of degreesof freedom. This enables the algorithms to explore a much larger space ofstructures and therefore design devices with higher efficiencies, smallerfootprint, 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 environmentsto 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.

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Installation¶

Simply clone the SPINS-B repository and run pip:

Getting Started¶

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

More documentation is forthcoming.

General Concepts¶

• Optimization plan: The optimization plan defines all the photonicoptimization problem (i.e. simulation region and desired objective) as wellas the sequence of optimization steps to achieve that objective. You definean optimization plan which is then executed by SPINS-B. Doing so enablesyou to have an exact record of all the parameters used to design a deviceas well as the ability to resume optimization if the optimization failsmidway.
• Simulation space: The simulation space defines the simulation regionas well as the design region (see below).
• Design area and design region: The design region is the region of thepermittivity distribution that is allowed to vary during the optimization.The design region is defined as the difference between two permittivitydistributions: Where the difference is non-zero corresponds to the designregion. Since most photonic devices are fabricated using top-down lithography,SPINS-B by default (this can be changed) assumes that the permittivitydistribution along the z-axis is the same, and hence we speak of adesign area.
• Parametrization: The parametrization defines how to describe thepermittivity of the design area. The simplest parametrization is to simplydescribe 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 whereasfield monitors post-processes vector field data and can select out aparticular plane to save data.
• Transformation: Optimization in SPINS-B actually consists of a sequenceof 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 commercialuse. It is a superset of SPINS-B and includes the ability to design deviceswithout writing any code with user-friendly interfaces and to apply precisefabrication constraints (minimum gap and curvature constraints). All devicesshown in our published work rely on capabilities found in the fully-featuredSPINS.

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 concernedwith device operation in a small bandwidth at particular frequencies. TheFDFD method is often faster than the more widely used finite difference timedomain (FDTD) method in these cases.

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

Publications¶

Any publications resulting from the use of this software should acknowledgeSPINS-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).