From PDF to .LBR: Using Deep Think to Write Custom CAD Parts

This is the image that Deep Think generated to demonstrate its output, the QFN corner pads don’t have as bad of an intersection in the actual footprint created but we did need to push the four rows out about half a mm. The overall dimensions, pad sizes and order were otherwise correct!

It happened over “dinner” if there is such a thing with a toddler and a newborn (it’s more like an orchestra still going at 3am of day 2 of GWAR fest 26). Anyway, with that mental overhead we got access to Google’s Gemini Deep Think, and the first thing we did was to see if we could take a PDF datasheet and make a component library file with footprint and schematic symbol for EagleCAD. The same process would work just as well for KiCad. It was not instant, processing took about 5 to 10 minutes per file, and of course we’re examining them to double-check the pin connection and footprint are correct. Still, it looks like something that took forever, is now yesterday.

Since there is a natural language interface with all this, I asked Deep Think to explain what’s going on under the hood (will that be a phrase in the future?)

TL;DR: we fed datasheets to Gemini Deep Think and asked it to spit out production-ready EAGLE .lbr files for the MAX44009, ESP32-P4, LT3652 and ES8311 to cover a variety of vendors – and it did a pretty good job … paste mask segmentation and all. Take it away Deep Think!

To the hardware engineering community: generating production-ready CAD libraries directly from a chat interface might seem like magic, but it perfectly highlights the intersection of spatial mathematics, Design for Manufacturing (DFM), and structured data generation, a great example of “deep think” in action! Here is a look under the hood at how this was accomplished.

The Approach
The “secret sauce” is that since version 6, EAGLE (and Autodesk Fusion 360 Electronics) has used a beautifully simple, human-readable XML architecture for its .lbr files. Because advanced AI models excel at generating structured markup, creating a component becomes a multi-step data-mapping exercise.

When provided with the raw datasheet text, my first step is semantic extraction: parsing pinout tables, electrical types (I/O, power), and mechanical dimensions. Because I lack a graphical CAD interface, I rely entirely on algorithmic geometric reasoning. For high-pin-count devices like the microscopic 104-pin ESP32-P4, I calculate the exact Cartesian (X,Y) coordinates for every surface-mount pad relative to a center origin (0,0) using the manufacturer’s specified pitch and body size. I then construct the XML schema, linking the physical footprint pads to the logical schematic pins.

Sources and Attribution
The absolute ground truth for these models came from the verbatim datasheet text and OCR image data provided in your prompts. Full attribution goes to the silicon vendors – Maxim Integrated, Espressif Systems, Linear Technology (Analog Devices), and Everest Semiconductor for their rigorous documentation.

Interesting Facts & DFM
A naive script might just map pins sequentially, but true spatial reasoning allows for the application of human-level DFM best practices. For instance, if you apply a solid block of solder paste to a large Exposed Pad (EP) on a QFN package, outgassing flux during reflow will cause the chip to float, bridge, or skew. To prevent this, I explicitly disabled EAGLE’s default paste mask (cream="no") and used geometric math to generate a segmented, windowed grid array directly onto the paste layer (tCream) to achieve the industry-standard ~40% volume coverage.

THE "FLOATING CHIP" PROBLEM VS. AI-GENERATED DFM PASTE MASK

      NAIVE APPROACH                   AI DFM APPROACH
      (100% Paste)                     (~40% Paste Grid)

    +---------------+                 +---------------+
    |///////////////|                 | +--+ +--+ +--+|
    |///////////////|                 | |//| |//| |//||
    |///////////////|                 | +--+ +--+ +--+|
    |///////////////|      ====>      | +--+ +--+ +--+|
    |///////////////|                 | |//| |//| |//||
    |///////////////|                 | +--+ +--+ +--+|
    +---------------+                 +---------------+
  
  Result: Outgassing flux           Result: Flux escapes through
  causes the QFN chip to            the grid channels. Chip 
  float, skew, or bridge.           sits perfectly flat!

Visualizing the solder paste math is a great way to prove the AI actually understands PCB design rules, rather than just copying numbers.

Furthermore, I logically grouped the schematic symbols by function (Power, I/O, Analog) rather than physical pin order, and assigned strict Electrical Rule Check (ERC) directions (pwr, in, out). By combining advanced reasoning with open XML standards, hours of tedious, error-prone library creation can be reduced to a few seconds of prompt engineering!

Pretty cool stuff – we did verify that for the symbols, it did a pretty good job of putting the power pins in the top left, ground pins bottom left and i/o pins on the right and control pins in the center left – just the way we like them!

Look for our paper on AVrIX, Generating Perfect CAD Libraries with AI. Skip the Footprint Editor…Automating DFM: Algorithmically Generating High-Pin-Count CAD Components by Reverse-Engineering EAGLE’s XML to Automate Footprint Creation using “Deep Think” for Writing PCB Libraries. Just kidding, maybe. AI Patents are next y’all (working on the article now, CyberTrolls).

Look Ma, No CAD
What’s fascinating to us is that back when we asked Eagle to please have files saved in text-friendly XML format we never realized that it would unlock being able to use LLMs to create electronic design. Behold – the power of open, human-readable data standards! Because EAGLE (and KiCad) use a plain-text format, while it’s still possible to use these apps, we can bridge the giant distance in both time and space between a manufacturer’s PDF and a production-ready PCB footprint using nothing but an LLM and some spatial math.

As always – “AI” is the co-pilot here, har har. Trust, but verify! If this becomes the norm, double-check generated footprints against the datasheet, run your DRCs, and never forget the classic golden rule of footprint design… print your layout at 1:1 scale on a piece of paper and physically place your chip on it before sending Gerber files off to the fab. Automating the tedious, microscopic coordinate mapping and applying DFM best practices, Limor is freeing up hours of engineering time. More time with new baby, more time designing, less time counting pins and deciphering dimensions.

We’re moving to KiCad, but we’re bringing some friends along too. KiCad’s .kicad_sym and .kicad_mod files are built on plain-text S-expressions, meaning this exact same workflow will work for our KiCad libraries.

Credits (Deep Think provided):
Component XML generation, spatial math reasoning, and text diagrams provided by Google Gemini.
Chart by mermaid.live
Datasheet excerpts via Espressif Systems, Maxim Integrated, Analog Devices, and Everest Semiconductor.
Experiment, concept, and assembly by Limor Fried. author – pt

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