https://github.com/aws-cqc/devicelayout.jl
Julia package for computer-aided design of quantum integrated circuits
https://github.com/aws-cqc/devicelayout.jl
cad eda gds gdsii julia microwave-engineering quantum quantum-computing qubits
Last synced: 18 days ago
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Julia package for computer-aided design of quantum integrated circuits
- Host: GitHub
- URL: https://github.com/aws-cqc/devicelayout.jl
- Owner: aws-cqc
- License: mit
- Created: 2025-02-21T19:25:04.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-06T22:20:47.000Z (about 2 months ago)
- Last Synced: 2025-03-06T22:28:08.412Z (about 2 months ago)
- Topics: cad, eda, gds, gdsii, julia, microwave-engineering, quantum, quantum-computing, qubits
- Language: Julia
- Homepage: https://aws-cqc.github.io/DeviceLayout.jl
- Size: 3.72 MB
- Stars: 14
- Watchers: 3
- Forks: 3
- Open Issues: 28
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
- Code of conduct: CODE_OF_CONDUCT.md
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README
# DeviceLayout.jl
[](https://aws-cqc.github.io/DeviceLayout.jl/stable)
[](https://aws-cqc.github.io/DeviceLayout.jl/dev)
[](https://github.com/aws-cqc/DeviceLayout.jl/actions/workflows/CI.yml)
[](https://github.com/JuliaTesting/Aqua.jl)
[](https://codecov.io/gh/aws-cqc/DeviceLayout.jl)
DeviceLayout.jl is a [Julia](http://julialang.org) package for computer-aided design (CAD) of quantum integrated circuits, developed at the AWS Center for Quantum Computing. The package supports the generation of 2D layouts and 3D models of complex devices using a low-level geometry interface together with a high-level schematic-driven workflow.
## Why use this package?
DeviceLayout.jl provides functionality for 2D/2.5D device CAD, including generation of GDS layouts for fabrication as well as 3D models for electromagnetic simulation. Package development aims to allow designers to produce and iterate on layouts quickly and easily, with particular attention to scalability in support of larger quantum processors and growing, collaborative teams. Key features include:
- Geometry-level layout with rich geometry types like polygons, ellipses, and paths
- Schematic-driven layout, allowing users to manage complexity by maintaining separate levels of abstraction for component geometry and device connectivity
- 3D modeling and meshing (via [Open CASCADE Technology](https://dev.opencascade.org/) and [Gmsh](https://gmsh.info/)), which takes advantage of rich geometry and schematic information to improve meshing and allow programmatic generation of configurations for simulation software (like [*Palace*](https://awslabs.github.io/palace/stable/), an open-source tool for electromagnetic finite-element analysis also developed at the AWS CQC)
- Built-in support for common elements of superconducting quantum processors like coplanar waveguides, air bridges, and flip-chip assemblies
- GDSII and graphics format export for 2D layouts, as well as various standard formats for 3D models and meshes
- Explicit unit support without sacrificing performance
- Users write code in Julia, a scientific programming language that combines high performance and ease of use
- The [Julia package manager](https://pkgdocs.julialang.org/v1/) offers portability and reproducibility for design projects in collaborations of any size
- Teams can manage their own process design kit as a set of Julia packages in a private registry, leveraging the package manager for versioning process technologies and components
## Installation
Julia can be downloaded [here](https://julialang.org/downloads/). We support Julia v1.10 or later.
From Julia, install DeviceLayout.jl using the built-in package manager:
```julia
import Pkg
Pkg.activate(".") # Activates an environment in the current directory
Pkg.add("DeviceLayout")
```
We recommend [using an environment for each project](https://julialang.github.io/Pkg.jl/v1/environments/) rather than installing packages in the default environment.
The [DeviceLayout.jl documentation](https://aws-cqc.github.io/DeviceLayout.jl/) will help you get started. Examples can be found in the `examples` directory, with full walkthroughs in the docs, including a [17-qubit quantum processor](https://aws-cqc.github.io/DeviceLayout.jl/dev/examples/qpu17/) and [simulation of a transmon and resonator with Palace](https://aws-cqc.github.io/DeviceLayout.jl/dev/examples/singletransmon/).
## Performance and workflow tips
[KLayout](https://www.klayout.de/) is a free (GPL v2+) GDS viewer/editor. It watches
its open files for changes, making it easy to use as a fast previewer alongside DeviceLayout.jl.
The recommended IDE for Julia is [Visual Studio Code](https://code.visualstudio.com/)
with the [Julia for Visual Studio Code extension](https://www.julia-vscode.org/).
Since Julia has a just-in-time compiler, the first time code is executed may take much
longer than any other times. This means that a lot of time will be wasted repeating
compilations if you run DeviceLayout.jl in a script like you would in other languages. For
readability, it is best to split up your CAD code into functions that have clearly named
inputs and perform a well-defined task.
It is also best to avoid writing statements in global scope. In other words, put most of
your code in a function. Your CAD script should ideally look like the following:
```julia
using DeviceLayout, DeviceLayout.PreferredUnits, FileIO
function subroutine1()
# render some thing
end
function subroutine2()
# render some other thing
end
function main()
# my cad code goes here: do all of the things
subroutine1()
subroutine2()
return save("/path/to/out.gds", ...)
end
main() # execute main() at end of script.
```
In a typical workflow, you'll have a text editor open alongside a Julia REPL. You'll save the above code in a file (e.g., `mycad.jl`) and then run `include("mycad.jl")` from the Julia REPL to generate your pattern.
You'll iteratively revise `mycad.jl` and save your changes.
Subsequent runs should be several times faster than the first, if you `include` the file again from the same Julia session.