{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"},{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"}],"enrichment":{"capability":"SAX is a JAX-based simulator and optimizer for S-parameter circuits, primarily designed for photonic integrated circuits but applicable to any frequency-domain circuit simulation using scattering parameters.","skillfed_tags":["photonics","circuit-simulation","jax-based"],"use_cases":["Design and simulate photonic integrated circuits by composing waveguides, couplers, and other optical components into larger systems.","Optimize photonic circuit parameters (coupling ratios, waveguide lengths, phase shifts) to achieve target transmission or reflection spectra.","Perform frequency-domain S-parameter analysis on arbitrary circuits without writing custom matrix algebra.","Leverage JAX autodiff to compute gradients for circuit optimization or sensitivity analysis across wavelength ranges.","Combine circuit simulation with machine learning workflows via JAX's ecosystem (Optax, Flax, etc.)."],"what_it_does":"SAX wraps JAX to provide a functional, dictionary-based framework for simulating and optimizing circuits described by S-parameters (scattering matrices). It was built for photonic integrated circuits but works with any frequency-domain circuit model. Rather than defining custom data structures, SAX stays close to JAX's functional paradigm: you write component models as functions returning S-dictionaries, compose them into circuits via netlists, and optimize them using JAX's autodiff and XLA compilation.\n\nThe package handles the boilerplate of circuit composition and parameter passing, allowing you to define a directional coupler, waveguide, or other component once and then combine them into larger systems like Mach-Zehnder interferometers. You can then sweep parameters (wavelength, coupling strength, length) across ranges and plot transmission spectra or use JAX's optimization tools to tune component parameters for a target response.","worth_installing":"Yes, if you work with S-parameter circuit simulation or photonic design. SAX is actively maintained, has no known vulnerabilities, and offers a clean functional interface built on JAX's autodiff and XLA compilation. The dependency footprint is large but manageable. Install with caution if your environment has strict dependency constraints; otherwise, it is a solid choice for frequency-domain circuit work."},"id":"sax","links":{"html":"https://skillfed.io/packages/sax","md":"https://skillfed.io/packages/sax.md","pypi":"https://pypi.org/project/sax/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-09","license_spdx":null,"license_treatment":"permissive","name":"sax","python_support":"supports_current","summary":"Autograd and XLA for S-parameters"},"popularity":{"monthly_downloads":136305,"position":11400,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.18.2"}