Generate Your PCB
by Generative AI
Your idea will be GERBER files for production in few minutes.
Disclaimer
I hereby solemnly declare that GerberGPT is only a code name for scientific research, has not achieved any substantial results, and has not engaged in any commercial activities. The content below is primarily for declaring the start time, research field, and process of this scientific research.
—— Tony Wang, November 11, 2023
Prelude
The integration of ChatGPT into daily life is becoming increasingly noticeable, and like many others, I find myself deeply influenced by this trend. The remarkable advancements in generative AI have ushered in substantial improvements in work efficiency across various domains. This progression leads me to ponder the potential of applying generative AI technology to the realm of PCB (Printed Circuit Board) design and manufacturing. The intriguing possibility of automating or enhancing aspects of PCB creation using generative AI is a concept I am actively exploring. To this end, I have embarked on a series of experiments and theoretical explorations to understand how generative AI can revolutionize the way we design and produce PCBs.
Theory 1
Gerber File
A Gerber file is a standard file format used in the electronics industry to communicate design information to manufacturing for the fabrication of printed circuit boards (PCBs). It's named after the Gerber Systems Corporation, a company that first developed the format. The Gerber file format has become the de facto standard for PCB fabrication data and is supported by nearly all PCB manufacturing equipment.
Key characteristics of a Gerber file include:
- Layer Representation: Each layer of a PCB (such as copper layers, solder mask, silkscreen) is represented by a separate Gerber file. This allows precise control over each aspect of the board's production.
- Command and Coordinates: The code in the file includes a series of instructions for controlling the drawing of lines, drilling, etc. This means that the design of the PCB can be modified by editing the Gerber file. This provides me with an important entry point.
- Vector Format: The format uses vector-based images to represent shapes and lines, which is ideal for defining the precise geometries needed for PCB layouts.
- Aperture Information: The file includes definitions for all the shapes (apertures) used in the design. These shapes are used to draw the conductive traces, pads, vias, etc., on the PCB.
- Drill Data: Often accompanied by a separate drill file (not always in the Gerber format) that specifies the locations and sizes of holes to be drilled in the PCB.
Gerber files are crucial for PCB manufacturing as they provide a reliable and standardized way to transfer detailed electronic design from CAD software to the physical world of circuit board. One of the problems I need to solve is how to generate Gerber files for the following different purposes using generative AI, such as:
- GTL - Top Layer
- GBL - Bottom Layer
- G* - Middle Layer
- GP* - Internal Plane Layer
- GTO - Top Overlay
- GBO - Bottom Overlay
- GTP - Top Paste
- GBP - Bottom Paste
- GTS - Top Solder Mask
- GBS - Bottom Solder Mask
- GKO - Keep-Out Layer
- GM* - Mechanical Layer
- GPT - Top Pad
- GPB - Bottom Pad
- GD* - Drill Drawing
- GG* - Drill Guide
Next?
Let me think. When I use EDA software like Altium Designer or KiCad. I always need to export the board design as GERBER files for manufacture. What is GERBER? It's actually code standard for factory. It's CODE. It's CODE. It's CODE.
Theory 2
Prepare Dataset
Browse semiconductor websites and download datasheets for various chips. Then, organize the data into a database.
I have downloaded Excel files. As the data provided by different manufacturers varies, establish a unified database is my first try. This allows me to conveniently continue processing and optimizing the structure of electronic component data in the later stages. In the end, I can quickly generate JSON files for datasets.
- Data Source Semiconductor website has product directory like this. It's super helpful for me to get parts with basic specs.
- Data Structure Components, footprints, pins and ratings tables are created. Data in these database tables will be associated by relationships.
Footprint Processing
Next?
If I can gather enough data and reasonably organize them into JSON files based on different usage rules, such as for passive components like resistors, capacitors, and inductors, their JSON dataset structures will certainly be vastly different from those with IO chips. How to organize these datasets presents my second major challenge.
Progress
GerberGPT is currently in the data collection phase.
- Component Trained
- 0
- Footprint Trained
- 0
- Parameters Trained
- 0
- Pins Ruled
- 0
Theory 3
Schematic
My idea of using machine learning to predict connections between chip pins based on their characteristics and relationships is quite intriguing and feasible. Here are some key considerations and steps to implement:
Let's create a theory for defining connections between a Power Regulator pins and passive components like resistors, capacitors, or inductors using machine learning involves understanding both the electrical engineering principles and the capabilities of machine learning models. Here's an outline of such a theory:
- Component Characteristics: Identify the characteristics of each component, such as the number of pins, pin types, and pin functions. For instance, a power regulator typically has three pins: input, output, and ground. The input pin is used to supply power to the regulator, the output pin is used to supply power to the circuit, and the ground pin is used to complete the circuit.
- Machine Learning Model Design: Use a model that can understand the relationship between components. Graph Neural Networks (GNNs) could be ideal as they can represent the circuit as a graph with nodes (components) and edges (connections).
- Implement a learning algorithm that can identify patterns and rules from the training data, recognizing how different pins of a regulator are typically connected to various passive
Next?
I will use the open-source nanoGPT to attempt training with a collected and organized dataset of chip information. Then, by inputting a specific circuit design requirement, I will test whether nanoGPT can provide a reasonable Bill of Materials (BOM) collection.
Theory 4
Model Training
In training the model, we already know the dataset and the expected results. For instance, when considering only the Copper layer, disregarding any circuit issues and other layers, we aim to generate GERBER code for a QFN packaged chip. The dataset comprises the package data from the chip specification sheet, which strictly defines the chip dimensions, the type, size, and spacing of pads, etc. The desired outcome is the GERBER format code that we need. Our task is to train the model to restrict the rules for generating the GERBER file.
Key characteristics I should keep in mind during the training process:
- Data Representation: Ensure the dataset accurately represents the chip specifications, including dimensions, pad types, sizes, and spacings. The model needs to understand these parameters in the context of generating GERBER code.
- Model Architecture: Choose a model architecture suitable for the task. Transformer might be beneficial.
- Loss Function: Define a loss function that accurately measures the difference between the generated GERBER code and the expected code. Since this is a generation task, I might consider custom loss functions that can handle structured outputs.
Next?
I will record various results and situations encountered during the model training process into videos for your reference.
Boost this Project?
If you possess more advanced technical capabilities and are willing to join the research, or if you are willing to provide financial support. If you wish to participate, please contact me.
