# AI Auto-Layout Winter Update AI Auto Layout delivers cleaner routing, faster results, and human-like PCB layouts, helping engineers create manufacturable designs with less effort. Written by [Ryan Fitzgerald](https://www.flux.ai/ryanf) Published Nov 2025 - 9 min read [![An astronaut placing electronics components and tracing complex signals on a complex glowing PCB board.](https://www.flux.ai/p/assets/blogs/ai-auto-layout-v2/main.webp)](#) ## When we launched the [preview of AI Auto Layout](https://www.flux.ai/p/blog/introducing-ai-auto-layout), we set out to make laying out a PCB feel as natural as sketching an idea. Instead of relying on fixed algorithms, we introduced an AI system that could route your board with clean, human-like patterns via a single click. Thousands of you put it through its paces. You tried it on real boards, found its edges, and told us exactly what would make it more useful in your workflow. Your feedback shaped every improvement in this release. ### What’s improved - Cleaner routing with shorter traces, fewer vias, and paths that follow placement intent - More legible layouts that are easier to review and refine - More results that are manufacturable as is, with far less cleanup when they aren’t - 4x faster convergence that reaches strong solutions more reliably This update builds on the same vision as the first release. Layout should feel intuitive from setup to review, and AI should handle the repetitive work so you can stay focused on design. ## Setting your board up for success This version of the model is not designed to fully autoroute every class of board without your input. You should still expect to step in for high speed interfaces, tight length matching, unusually thick power nets that land on small pads, and designs with strict layer reservations. The sweet spot today is a workflow where you define the important constraints and critical paths, then let AI Auto Layout finish the remaining 80 percent. The more context you give AI Auto Layout, the better the results. When rules are realistic, sensitive areas are protected, and critical signals are handled up front, the router can focus on what it does best: finishing the busy work in a way that looks like a human EE’s layout. If you want to see what a well-prepared setup looks like, check out this [*ESP32 Espresso Smart Scale*](https://www.flux.ai/example-user/esprsso32-smart-scale-ai-auto-layout-example?editor=pcb_2d) project. You can even run AI Auto-Layout to see it perform it’s magic. [ ![ESP32 Espresso Smart Scale project, an example project prepared to run AI Auto-Layout](https://www.flux.ai/p/assets/blogs/ai-auto-layout-v2/setup-image.webp) ](https://www.flux.ai/example-user/esprsso32-smart-scale-ai-auto-layout-example?editor=pcb_2d) *Learn how to use AI Auto Layout on this* [*ESP32 Espresso Smart Scale*](https://www.flux.ai/example-user/esprsso32-smart-scale-ai-auto-layout-example?editor=pcb_2d) Here is a quick checklist to prepare your board before you run Auto Layout. 1. **Define rulesets that reflect reality** - Set widths, clearances, and via sizes per net class. Make sure pins are routable with those choices so the router does not get trapped by impossible geometry. [Learn more](https://docs.flux.ai/reference/layout-rules-types) 2. **Add keepouts where it matters** - Protect the areas that are most sensitive. Under RF, oscillators, switchers, and around high voltage nets. Keep aggressors away from victims so the router has clear guidance about where not to go. [Learn more](https://docs.flux.ai/tutorials/working-with-polygons#keep-outs-and-polygons) 3. **Fan out fine pitch parts** - Give the router room to move. Escape dense packages so trace widths and escape routes do not fight each other. A little manual fanout goes a long way for the rest of the job. 4. **Manually route truly critical signals** - Handle the nets where mistakes are expensive. Differential pairs, tight timing paths, controlled impedance lines, and anything that must follow a very specific path. 5. **Click Auto Layout and review the result** - Once the context is in place, let AI Auto Layout complete the rest. Use it to fill in the bulk of the routing, then make small, surgical edits where you want to enforce personal or team style. [ ![ESP32 Espresso Smart Scale, an example PCB project that has been auto-routed using Flux AI Auto-Layout](https://www.flux.ai/p/assets/blogs/ai-auto-layout-v2/auto-layout-solution.webp) ](https://www.flux.ai/flux/esprsso32-smart-scale-ai-auto-layouted?editor=pcb_2d) *Click on the* [*project above*](https://www.flux.ai/flux/esprsso32-smart-scale-ai-auto-layouted?editor=pcb_2d) *to check out the AI Auto-layout results yourself* Learn more about how to setup your project by watching this [video tutorial](https://youtu.be/JlNsPRh_x-w) or reading the [documentation](https://docs.flux.ai/tutorials/auto-layout) on AI Auto-Layout. ## Better, more manufacturable results Traditional autorouters treat routing as a geometry puzzle. They chase shortest paths and resolve constraints, but ignore the realities that matter to engineers: manufacturability, signal behavior, and the ability to understand the layout at a glance. The result is familiar. Tangled traces. Excessive vias. AI Auto Layout approaches the problem differently. It learns patterns that resemble human decision-making and adapts its choices as it routes, which leads to results that feel deliberate, balanced, and easier to work with. This update sharpens that behavior. The routing is cleaner, straighter, and more aligned with your placement intent. You will see fewer unnecessary layer transitions, more predictable via placement, and stronger power paths. Layouts are easier to review, reason about, and move toward manufacturable results. In many cases, the output looks like a thoughtful first pass from an experienced engineer. #### Examples of AI Auto-Layout’s Human-like results: [ ![A pcb board with traces on 2D, pointing out how AI Auto-Layout routed the sensitive 5V0 and 3V3 rails with solid continuity.](https://www.flux.ai/p/assets/blogs/ai-auto-layout-v2/digital-power.webp) ](https://www.flux.ai/ryanf/esprsso32-smart-scale-ai-auto-layout-prodtest11-15-25?editor=pcb_2d) **Clean Power Distribution:** Auto-Layout routed the 5V0 and 3V3 rails with solid continuity, avoiding broken return paths and keeping them isolated from noisy elements like the antenna and full-speed SD card. The result is stable power delivery with fewer opportunities for interference. [ ![A pcb board with traces on 2D, pointing out how AI Auto-Layout routed the sensitive signals around high-power copper polygons.](https://www.flux.ai/p/assets/blogs/ai-auto-layout-v2/routing-around-high-power-copper.webp) ](https://www.flux.ai/ryanf/usb-type-c-pd-dfp-prodtest11-16-25?editor=pcb_2d) **Reliable Signals Across High-Power Copper:** Auto-Layout navigated routing around high-power copper while preserving the integrity of critical feedback signals. The results are cleaner paths, fewer compromises, and a design that behaves exactly as intended. [ ![A pcb board with traces on 2D, pointing out how AI Auto-Layout routed the traces with less layer transition, meaning less vias for lower production costs.](https://www.flux.ai/p/assets/blogs/ai-auto-layout-v2/less-layer-trajectory.webp) ](https://www.flux.ai/ryanf/pico-smart-automation-controller-prodtest11-15-25?editor=pcb_2d) **Less Layer Transitions and Vias**: By intelligently routing traces, it creates cleaner signal paths, improves electrical performance, and helps simplify manufacturing. The result is a more efficient, robust board design with fewer potential points of failure and often lower production costs. [ ![A pcb board with traces on 2D, pointing out how AI Auto-Layout routed the high-speed signals, MIPI-CSI and ESP32's QSPI bus.](https://www.flux.ai/p/assets/blogs/ai-auto-layout-v2/high-speed.webp) ](https://www.flux.ai/ryanf/pico-smart-automation-controller-prodtest11-15-25?editor=pcb_2d) **High-Speed Signal Pathfinding:** Auto-Layout roughed in clean first-pass routes for medium-complexity boards, including a 3-lane MIPI-CSI interface, the ESP32’s QSPI bus, and its critical clock lines. It maintained proper layer separation, avoiding routing traces directly beneath each other on adjacent layers to help preserve signal quality. ## Time and credit usage AI Auto Layout is faster in this release. On well prepared boards, the new model reaches strong solutions sooner and stops when further search is unlikely to improve quality. You avoid long, wandering runs and get to a clean layout much more predictably. On simple two layer boards without sensitive nets, you should expect a complete route in minutes. Boards with dense placement, large trace widths, or strict layer reservations will take longer, but the model now recognizes when it has converged so it avoids wasting credits on unproductive search. AI credit usage varies based on design complexity, routing constraints, and the length of the run. Here are reference points to help you plan. #### Average credits used per board complexity | Layers | Average Components | Average AI credits | | :--- | :--- | :--- | | 4-layer | 40 | 375 | | 4-layer | 100 | 750 | | 2-layer | 40 | 625 | | 2-layer | 100 | 1250 | If your board includes complex exceptions, tight fanouts, thick power nets that must enter small pads, or manually reserved layers, it may require additional credits. You can always see your live credit usage while the job runs. This update makes both time and credits more predictable. When the router is progressing toward a strong solution, it continues. When it has reached a point of diminishing returns, it stops. That balance keeps your workflow moving and helps you budget runs more effectively across both two and four layer designs. ### Try the new AI Auto-Layout today! This update moves AI Auto Layout closer to the experience many of you have been asking for: cleaner routing, better electrical behavior, faster convergence, and layouts that move you toward a manufacturable board with less effort. It now handles both two and four layer designs more reliably, especially when you provide clear rules and route your most sensitive nets before handing off the rest. We would love to hear how it performs on your next project. Try it on a real board, share your results, and tell us what you want to see next. Your feedback continues to shape every release. [Try it out today](https://www.flux.ai/) and let us know what you think. [Try AI Auto-Layout today!](https://www.flux.ai/) ### Frequently asked questions #### 1\. What makes AI Auto-Layout different from traditional autorouters? Traditional autorouters focus on connectivity and shortest paths, which often produces layouts that are tangled, brittle, or difficult to review. AI Auto Layout reasons over placement, net classes, and electrical intent. It places vias more intentionally, follows natural routing channels, and stops when it reaches a strong solution. The result looks closer to a thoughtful first pass from an experienced engineer. #### 2\. What kinds of boards can it route today? AI Auto Layout performs well on many two and four layer designs, especially when you provide clear rulesets and manually route the most sensitive nets first. It can fully route simple and moderately complex boards. Larger or more constrained designs also work well when critical nets, tight timing paths, or strict layer reservations are guided by hand before running the AI. #### 3\. How fast is it? On well-prepared two layer boards without sensitive nets, most runs complete in minutes. Four layer and up designs vary more based on placement density, routing constraints, and net priorities. The model is tuned to recognize diminishing returns, so it avoids wasting credits. #### 4\. How many AI credits does it use? Credit usage depends on component count, constraints, and overall geometry. Typical ranges: - Around 80 to 150 credits for simple two layer boards with about 30 components - Around 150 to 250 credits for moderately dense two layer boards with around 50 components - Layer count by itself does not always increase credit usage. For similar designs, four layer boards often complete routing faster than very dense two layer boards, so credit usage can be similar or even lower. Credits are driven mostly by component count, net complexity, and your design rules. You can see your live credit usage while the job runs. #### 5\. What should I do before running AI Auto-Layout? You will get the best results if you: - Set realistic rulesets for widths, clearances, and via sizes - Add keepouts around sensitive or noisy areas - Fan out dense or fine pitch components - Manually route nets that require tight electrical control - Then run Auto Layout to complete the rest This mirrors the workflow most teams use when blending manual routing with AI assistance. #### 6\. Can I add my own constraints? Yes. You can define rulesets by net class, set trace widths and clearances, choose via sizes, add zones and keepouts, and guide placement with manual fanouts. Clear, realistic constraints significantly improve results. #### 7\. Is my project data used for training? Throughout this process, your data remains yours. We employ robust encryption and modern data centers to keep your information secure, just like the trusted cloud services you rely on daily. If you want to learn more, please take a look at our [Privacy Statement](https://docs.flux.ai/legal/privacy-statement). ## About the author [Ryan Fitzgerald](https://www.flux.ai/ryanf) Electronics Engineer at Flux Ryan is an electronics and electrical systems engineer with a focus on bridging the gap between deep learning intelligent algorithms and innovative hardware design. Find him on Flux [@ryanf](https://www.flux.ai/ryanf) Let’s start building Build your big idea together with Flux. All you need to do is describe what you want to make, or what problem you want to solve. Flux will work with you to make it a reality. ![Close-up photo of a PCB made with Flux, with an ESP32 module, buttons, and a microSD slot.](https://www.flux.ai/p/assets/blogs/miscellaneous/sidebar-cta-pcb.webp) [Try it Free](https://www.flux.ai/signup) ## More in AI & Prompt Engineering for Hardware [ ![What Makes a Perfect PCB Design Prompt](https://www.flux.ai/p/assets/blogs/how-to-write-a-perfect-pcb-design-prompt/main.webp) ### What Makes a Perfect PCB Design Prompt 8 Min Read](https://www.flux.ai/p/blog/what-makes-a-perfect-pcb-design-prompt)[ ![Flux, an AI hardware engineer pointing out the dos and don'ts when prompting.](https://www.flux.ai/p/assets/blogs/prompting-guide/main.webp) ### Prompting Guide: Working with Flux from Idea to Schematic 9 Min Read](https://www.flux.ai/p/blog/prompting-guide-working-with-flux-from-idea-to-schematic)[ ![A space-themed image with a central cartoon astronaut helmet against a backdrop of stars and nebulae in shades of purple. 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Let’s build your idea together. All you need to do is describe what you want to make, or what problem you want to solve. Flux will work with you to make it a reality. [Try it Free](https://www.flux.ai/signup) ![Close-up of a Flux-designed printed circuit board](https://www.flux.ai/p/assets/homepage-v2/mission-visual.webp)