{"slug": "we-automate-kicad-pcb-routing", "title": "We Automate KiCad PCB Routing", "summary": "AutoCuro detailed a KiCad AI plugin that automates printed circuit board placement and routing inside KiCad's native environment using the platform's Python scripting interface, without cloud uploads or external servers. The company said its pipeline parses .kicad_sch schematic files to extract connectivity and design intent before generating layout, and that it tested the tool on two open-source designs, including the Main Avionics Board from the Terrapin Rocket Team's Student Researched and Developed (SRAD) Avionics project. AutoCuro framed the effort as addressing KiCad's manual-only workflow, which it said has relied on external plugins such as FreeRouting that require significant manual setup.", "body_md": "## Why We Need Automation in KiCad?\n\nImagine finishing your schematic, importing it into your PCB editor, and instead of facing a three-day routing marathon, you define your core design rules, click a button, and watch as an intelligent agent places and routes your board in minutes. You're no longer a hard-working engineer doing repetitive tasks; you're a supervisor, free to focus on the high-level, critical aspects of your design. This isn't a fantasy from a million-dollar EDA suite. It's the future we believe is possible with AI PCB automation for KiCad, and it addresses the platform's most significant bottleneck: its entirely manual workflow. Unlike tools such as Altium or Cadence that offer some degree of autorouting, KiCad autorouting has historically depended on external plugins like FreeRouting—and even those require significant manual setup.\n\nIn our previous posts, we discussed [\\[how we automate altium ai pcb routing\\]](https://autocuro.com/blog/how-we-automate-altium-ai-pcb-routing), [\\[why pcb autorouting remains broken\\]](https://autocuro.com/blog/why-pcb-autorouting-remains-broken) and [\\[why autoplacement requires manual intervention\\]](https://autocuro.com/blog/why-pcb-component-autoplacement-still-requires-manual-intervention). While those challenges exist in commercial EDA platforms, KiCad doesn't even have these automation features to begin with. The question we're addressing today: **Can we bring effective AI PCB automation to KiCad's manual-only workflow?**\n\n## How Do We Add Automation to KiCad?\n\nFor any effective automatic PCB placement and routing, you need good placement first, then PCB autorouting. Without intelligent component placement, even the best routing algorithms will struggle to produce quality results. To achieve a good understanding of the design, we use the automation pipeline mentioned below and KiCad's Python scripting interface. Our KiCad autorouting process differs from traditional approaches in a fundamental way: we start by reading and understanding the schematic design intent before generating the physical layout.\n\n### Our Automation Pipeline\n\n#### 📄 Schematic Analysis\n\nWe parse KiCad schematic files (.kicad_sch format) to extract connectivity information, component relationships, and circuit hierarchies. This gives us context that pure netlist-based approaches miss.\n\n#### 🧠 Design Intent Extraction\n\nFrom the schematic data, we identify functional blocks, power distribution networks, high-speed interfaces, and critical signal paths. This understanding guides our placement and routing decisions.\n\n#### 🔧 KiCad Python Integration\n\nUsing KiCad's Python scripting capabilities, we programmatically generate the PCB layout, place components, and create routing paths. Our tool operates as a KiCad AI plugin, working within KiCad's native environment without requiring cloud uploads or external servers.\n\n### The Tool Interface\n\nThe AutoCuro KiCad Automation Suite provides a step-by-step workflow panel that guides engineers through each stage of the design process — from folder selection to Gerber generation.\n\n## Target Boards: What We Tested\n\nTo test and develop our automation approach, we have run it against two real-world open-source designs at different complexity levels. Both boards are documented as separate case studies. In this blog, we discuss the Main Avionics Board from the Terrapin Rocket Team's Student Researched and Developed (SRAD) Avionics project while you can refer to our latest case study [\\[Automated thunderbolt pcie adapter board design in kicad\\]](https://autocuro.com/case_studies/automated-pcie-adapter-board-in-kicad) which overcomes the limitations mentioned in the blog.\n\n### Board Specifications\n\nThis board includes power management, sensor interfaces, communication peripherals, and the complexity typical of aerospace applications. It's an ideal test case because it represents real engineering requirements rather than a simplified example.\n\n## What Are the Inputs for the App?\n\nOur automation system requires specific inputs from the KiCad design environment:\n\n#### 📋 Required Input Files\n\n- KiCad schematic files (.kicad_sch) - Contains circuit connectivity and hierarchy\n- Board file with board outline and component footprints kept outside the board outline\n- Design rules - Track widths, clearances and layer stackup. These are added through external document\n\n## How Do We Control and Review the Design Generation Process?\n\nUsing our app we allow the code to be run through various stages so that the user has control over every stage and does not waste time while running a large design cycle. Following stage outputs are available which can be iterated again and again: Placement, Routing and Gerber generation. Engineers need visibility into what the automation is doing and the ability to guide the process.\n\n### Reviews Done During Every Process\n\n#### 🔍 1. Check for Netlist Errors\n\nVerification of netlist integrity and connectivity before processing begins.\n\n#### 📚 2. Library Mismatch\n\nDetection of component footprint and library inconsistencies.\n\n#### 📊 3. Placement Scores\n\nGenerated based on the dynamic rules created for the database.\n\n#### 🏭 4. DFA/DFM Checks\n\nDesign for Assembly and Design for Manufacturing validation.\n\n#### ✅ 5. Routing Scores\n\nEvaluation of routing quality and completion metrics.\n\n## How Do the KiCad AI Placement Results Look?\n\nPlacement quality directly impacts routing success. Our approach focuses on understanding circuit functionality to create logical component groupings.\n\n### Placement Features\n\n#### 🎯 Circuit-Aware Grouping\n\nComponents are grouped based on their functional relationships identified from the schematic. Power management circuits stay together, sensor interfaces are clustered, and the MCU support circuitry is properly organized around the processor.\n\n#### ⚡ Critical Component Positioning\n\nDecoupling capacitors are automatically placed close to their associated power pins. Crystal oscillators and their load capacitors maintain proper geometric relationships.\n\n## How Does the KiCad Autorouting Look?\n\nAfter placement, our KiCad autorouting algorithms do the following: Power plane decision, power supply fanouts, stackup selection if not input, differential nets routing strategy, via optimization, routing of traces, cleanup. If you've used the KiCad FreeRouting plugin before, the overall stage sequence will feel familiar—but the schematic-driven decisions are what differentiate this approach.\n\n### Routing Completion\n\nThe AI PCB automation completed routing across signal layers and power planes. However, like all PCB autorouting approaches, not everything was perfect on the first pass.\n\n## What's Great About This Automation?\n\n### ✅ Genuine Advantages\n\n#### What Does the Tool Automate?\n\nThe tool automates component placement and PCB routing, running entirely from your desktop. Starting from your KiCad schematic and input design constraints, it handles: component placement across the board (balancing mounting hole positions, edge components, functional block grouping, power distribution, and differential routing constraints), followed by power plane generation and complete trace routing. The tool also generates DFA and DFM reports as part of the output. Tasks that would typically take a layout engineer half a day to a full day for placement alone are handled in approximately 5 minutes, depending on the system.\n\n#### Schematic-Driven Intelligence\n\nBy starting with schematic analysis, the AI PCB automation understands circuit intent. This produces more logical placement and routing decisions compared to purely netlist-based approaches.\n\n#### IP Protection\n\nWe generated this design using automation algorithms which run from your system, keeping your design data secure on your own hardware.\n\n#### Minimal Configuration Setup\n\nUnlike traditional PCB autorouting tools that require extensive constraint definition, our approach extracts most requirements directly from the schematic. Engineers spend less time configuring the tool and more time reviewing results.\n\n#### Integrated Workflow\n\nThe AI PCB automation process handles both placement and routing in a coordinated manner, considering how placement choices affect routing outcomes. This integration improves overall results. The algorithm is known to work reliably on latest KiCad versions ver8 and ver9.\n\n## What Part of the Design Was Manually Created?\n\nTransparency is important. Our automation doesn't eliminate the need for engineering judgment—it reduces repetitive work.\n\n### Manual Design Elements\n\n#### 🎨 Board Outline Definition\n\nThe mechanical board shape, mounting holes, and connector locations were defined manually based on the enclosure requirements. Automation needs these boundaries to work within. Board footprints are expected by default.\n\n#### 📍 Predefined Constraint\n\nYou can suggest connector or component positions beforehand by defining the rough location where you want certain components. This allows the automation to work within your preferred layout structure while optimizing the surrounding placement.\n\n#### ⚙️ Design Rules Setup\n\nDesign rules are set up using an external document which the app reads while running. This allows for easy configuration of track widths, clearances, and via sizes specific to your project requirements.\n\n#### 🔧 Final Placement Refinement\n\nThe automated placement was modified for crystal and decoupling capacitors, as well as power blocks to make them more routable. These adjustments ensured optimal signal paths and power distribution.\n\n#### ✏️ Final Routing Refinement\n\nAfter automated routing, the design required manual review and cleanup. Differential traces, even though routed, needed refinement. Power planes on the top layer had to be redrawn. Some signals had to be rerouted for optimal signal integrity and electrical performance.\n\n```\nTime Investment Reality\nThe automated portion (placement + routing) took approximately 10 minutes. Manual refinement and optimization required an additional 3 hours of engineering time. This is still significantly faster than full manual design, which could take 16-24 hours for a board of this complexity.\n```\n\n## What Still Needs Work?\n\nWe're committed to honest assessment of our technology. Here are the areas where improvement is needed. Several limitations identified on this STM32 avionics board — including differential pair length matching and power plane handling for voltage regulators — have since been addressed. For the latest results, see our [Automated Thunderbolt PCIe Adapter Board Design in KiCad](https://autocuro.com/case_studies/automated-pcie-adapter-board-in-kicad) case study.\n\n### ⚠️ Current Limitations\n\n#### Board Complexity\n\nIf the size of the board reduces wherein the placement has to be done, it is possible that the current algorithm would struggle. We would be adding another board study which would show more components on a board having higher component and routing complexity.\n\n#### Scalability Testing Ongoing\n\nThis avionics board represents moderate complexity. We're continuing to test the automation with larger designs to understand scalability limits and identify optimization opportunities.\n\nBeyond routing completion, the tool also flags post-route issues that a standard DRC (Design Rule Check) will not catch — such as clock net stubs, differential pairs crossing reference-plane voids, power-plane splits that interrupt return paths, and floating vias. Some of these are auto-corrected during the routing run; others are highlighted for engineer review. See [PCB Design Errors Your DRC Won't Catch](https://autocuro.com/blog/pcb-design-errors-drc-wont-catch) for a detailed walkthrough of what gets flagged and why.\n\n## How Do We Compare?\n\nAn honest comparison of PCB autorouting vs manual routing requires acknowledging that different approaches have different strengths. Here's how KiCad autorouting options stack up:\n\n### KiCad Manual Routing\n\n- Complete engineer control over all decisions\n- Optimal results for experienced designers\n- Time-intensive: 16-24 hours for this board complexity\n- Requires deep understanding of design intent\n- No automated assistance for repetitive tasks\n\n### KiCad FreeRouter Plugin\n\n- Rule-based PCB autorouting without schematic awareness\n- Effective for simple or highly constrained routing tasks\n- Requires significant setup and tuning of design rules\n- Limited understanding of placement and signal intent\n- Often produces suboptimal results requiring heavy cleanup\n\n### KiCad AI PCB Automation\n\n- Schematic-driven AI PCB automation provides design context\n- Integrated placement and PCB autorouting optimization\n- ~10min automation + 3 hours refinement\n- Works within KiCad's native environment\n- Still requires manual optimization for production\n\n```\nThe Honest Assessment\nOur AI PCB automation doesn't replace skilled PCB designers. Instead, it handles the repetitive aspects of layout work, allowing engineers and managers to increase productivity and reduce layout time and shorten time to market.\n```\n\n## Frequently Asked Questions\n\n### Want to See More—or Try It Yourself?\n\nExplore more PCB designs created using our AI-powered PCB automation and autorouting technology, or try the beta version of our app and see the workflow in action.\n\n[Try the Beta App](https://autocuro.com/app)\n\n[View More Case Studies](https://autocuro.com/case_studies)", "url": "https://wpnews.pro/news/we-automate-kicad-pcb-routing", "canonical_source": "https://autocuro.com/blog/how-we-automate-kicad-pcb-routing", "published_at": "2026-09-15 04:06:15+00:00", "updated_at": "2026-09-15 04:33:49.225800+00:00", "lang": "en", "topics": ["ai-tools", "ai-products", "developer-tools", "ai-agents"], "entities": ["AutoCuro", "KiCad", "AutoCuro KiCad Automation Suite", "FreeRouting", "Altium", "Cadence", "Terrapin Rocket Team", "Main Avionics Board"], "alternates": {"html": "https://wpnews.pro/news/we-automate-kicad-pcb-routing", "markdown": "https://wpnews.pro/news/we-automate-kicad-pcb-routing.md", "text": "https://wpnews.pro/news/we-automate-kicad-pcb-routing.txt", "jsonld": "https://wpnews.pro/news/we-automate-kicad-pcb-routing.jsonld"}}