{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"}],"enrichment":{"capability":"KFactory is a Python framework for designing photonic and electronic chip layouts, providing parametric cells, routing primitives, cross-section definitions, and schematic-driven design with layout verification on top of KLayout's geometry engine.","skillfed_tags":["chip-design","photonics","parametric-layout"],"use_cases":["Design parametric photonic circuits with automatic cell deduplication and caching","Route optical waveguides and electrical traces with Manhattan and all-angle primitives","Define reusable cross-sections and automatically generate cladding layers via Minkowski sums","Extract netlists from physical layouts and verify consistency with schematics","Develop technology PDKs that bundle layers, factories, and design rules for reuse","Interactively preview and edit chip layouts in Jupyter notebooks with KLive integration"],"what_it_does":"KFactory is a Python framework for designing photonic and electronic chip layouts, built on KLayout's C++ geometry engine. It abstracts chip design into parametric cells that can be cached and reused, provides routing algorithms for optical and electrical paths, and supports cross-section definitions for waveguide profiles. The framework integrates schematic-driven workflows with netlist extraction and layout-vs-schematic verification, allowing designers to move between logical schematics and physical layouts.\n\nThe package works with both integer (DBU) and floating-point (\u00b5m) coordinate systems, includes Jupyter integration for live preview in KLayout, and bundles a PDK system for packaging technology definitions. It requires Python 3.12 or later and depends on 18 runtime packages spanning geometry (scipy, rectangle-packer), configuration (pydantic, ruamel-yaml), and version management (semver). No known security vulnerabilities are reported.","worth_installing":"Yes, if you are designing photonic or electronic chips and want a Python-native parametric layout framework. The active maintenance, low install friction, and rich feature set (routing, schematics, verification) make it a solid choice for chip design workflows. Verify the license treatment before use in proprietary contexts, and confirm klayout system dependencies match your environment."},"id":"kfactory","links":{"html":"https://skillfed.io/packages/kfactory","md":"https://skillfed.io/packages/kfactory.md","pypi":"https://pypi.org/project/kfactory/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-27","license_spdx":null,"license_treatment":"unclear","name":"kfactory","python_support":"supports_current","summary":"KLayout API implementation of gdsfactory"},"popularity":{"monthly_downloads":242878,"position":8833,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.0.4"}