Where to Find Pcb Dataset for Autorouting?
Posted4 months agoActive3 months ago
Techstory
calmmixed
Debate
60/100
Pcb AutoroutingAI in Electronics DesignCad Software
Key topics
Pcb Autorouting
AI in Electronics Design
Cad Software
Hi, I'm completely new to PCB autorouting and just started exploring it. I've figured out that AI is now a big thing for predicting routes, but it seems that many researchers are facing issues with datasets.
I found some of their GitHub links, but the datasets are either missing or the code to generate them is broken.
Is there any way to get PCB datasets specifically for autorouting? I've seen many datasets for PCB defects, but not for this purpose.
The author is seeking a dataset for PCB autorouting and the discussion revolves around the challenges and limitations of autorouting, including the need for AI, constraint setting, and the complexity of the problem.
Snapshot generated from the HN discussion
Discussion Activity
Active discussionFirst comment
3d
Peak period
11
72-84h
Avg / period
5.3
Comment distribution16 data points
Loading chart...
Based on 16 loaded comments
Key moments
- 01Story posted
Sep 23, 2025 at 6:27 PM EDT
4 months ago
Step 01 - 02First comment
Sep 27, 2025 at 1:26 AM EDT
3d after posting
Step 02 - 03Peak activity
11 comments in 72-84h
Hottest window of the conversation
Step 03 - 04Latest activity
Sep 28, 2025 at 3:27 PM EDT
3 months ago
Step 04
Generating AI Summary...
Analyzing up to 500 comments to identify key contributors and discussion patterns
ID: 45353604Type: storyLast synced: 11/20/2025, 3:50:08 PM
Want the full context?
Jump to the original sources
Read the primary article or dive into the live Hacker News thread when you're ready.
Second, you're describing netclasses. Every EDA package has this feature. You have to click one extra box when setting up your symbols.
But: 99.9% of the people forget about setting the routing constraints/rules so the results turn out to be very bad. Even professionals forget about this.
Lots of things also cannot be auto-routed as you have to work around of details. Usually high-frequency DCDC converters belong into that region for example.
As a beginner these are no tools for you as you cannot judge if they are creating extremely bad designs or what will work. Stick super close to reference designs from datasheets and there will be a good chance that they will work just fine.
Would love auto routing if it was good; routing is tedius, and on cramped PCBs, can be frustrating and make your designs high-inertia to change.
Incidentally, team 6+ layers! Makes routing easier.
Use the completed traces and part locations (complete with human post adjustments and all) as labels and the bare connectivity graph + "constraints" in some form as inputs.
Of course, as with all machine learning projects, the interface is deceptively simple but gives you no information how well the system can work or whether it can work at all...
If you are talking about a typical hobbyist board with very low frequency switching, the problem is relatively simple, particularly if you allow more than two layer boards. Even there you can create massive problems if you do power distribution wrong.
On the other hand, if you are trying to do something with high frequencies, the problem will be much more difficult. For example, many high speed analog-digital converters use low voltage differential signalling (LVDS) to move data around. Such signals are very sensitive to bad routing since they must be kept very near each other (to avoid creating an inductive loop sensitive to external signals) and must have the same length (to retain common mode rejection). Similarly, RF amplifiers will often oscillate badly or have much lower bandwidth if laid out incorrectly.
LLM would need to layout and route while board own. We would probably need diffusion or agentic solution where board can be simulated in RL loop.
What's not solved is setting up constraints on signals.
Now if you still want to do this anyway, I'd suggest building an autorouter and automatically generating custom parts with both standard and generated footprints, place them randomly on a PCB with a random number of layers, then autoroute it with a conventional algorithm and there is your dataset.