Overview

The goal is to predict silkscreen placement using a graph neural network. This is still in the testing/training phase. I can currently get the loss to decrease by several orders of magnitude, but it's not yet sufficient for accurate placement predictions. I welcome any contributions to attempt to solve this problem and believe solving silk placement would be a good proof of concept that component placement is solvable with this same architecture. Also, if anyone knows of altium or cadence projects that could be used in training please let me know.

Getting Started

• Export data using altium/cadence script or use the example.csv file in the data/train/ directory
• Add data to projects' data/training/ directory
• Add data to projects' data/test/ directory
• Run training notebook (check data preprocessing)
• Once training, if you have a dataset in data/test/ the model will predict and export to data/predictions/predictions.csv and can be imported w/ the altium import script.
• Check progress in Logs notebook

Data Collection

*Currently Supported PCB Tools: Altium & Cadence (Cadence only has export data script, not import script, and has not been as thoroughly vetted as altium data. I am less familiar w/ cadence)

• Use the export scripts in the scripts directory (Open pcb--> Run export)
• This project only uses the .PCBDoc [Altium] or .brd [Altium] file, so the full project does not need to be opened.

Downloadable Projects

Altium

• BeagleBone Black
• https://resources.altium.com/p/open-source-hardware-projects-in-altium-designer
• https://www.imx6rex.com (No Silkscreen Designators)

Cadence

• Unknown

Training

• Uses pytorch geometric to create a heterogeneous graph (different node types) of silkscreen, component, track, & arc nodes
• Predicts x, y, & rotation of the silkscreen designator (would like to predict size in the future)

Graph Network Architecture

• Heterogeneous graph
• Inputs both categorical & numerical features
• Set the gnn type, number of layers, etc. in model.py

Data Description:

Column	Type	Description
Tool	N/A	altium/cadence
Type	N/A	Features extracted from type Silk or Pin (x/y/L/R/T/B). If Pin Type, features may be extracted from parent Component
Designator	Component	Parent Component Designator
x/y	Component or Silk	Origin Coords
L/R/T/B	Component or Silk	Bounding Rectangle (x1,x2,y1,y2 for tracks/arcs)
Rotation	Component or Silk	Degrees
Layer	Component	Top or Bottom
Info	Type Specific	Delimited list of extra info specific to Type
Board	N/A	This is not imported data. This is an index of each board/dataset

Info Column Specific Data

Tool	Info Column	Type	Description
Altium	PinName	Pin	Pin Name
Cadence	PinNumber	Pin	Pin Number
Cadence	PadstackName	Pin	Padstack Name
Cadence	PinRotation	Pin	Rotation in Degrees
Cadence	PinRelativeRotation	Pin	Rotation in Degrees Relative to Component
Cadence	IsThrough	Pin	Is a Through Hole pin
Both	NetName	Pin	Pin's Net Name
Both	PinX/Y	Pin	Pin's Origin Coords
Both	Width	Track/Arc	Track/Arc Width
Altium	Length	Track	Track Length
Both	Radius	Arc	Radius in mils
Altium	StartAngle	Arc	Start Angle
Altium	EndAngle	Arc	End Angle
Cadence	IsCircle	Arc	CIRCLE/UNCLOSED_ARC
Cadence	IsClockwise	Arc	TRUE/FALSE
Both	InComponent	Track/Arc	Object Part of Component Footprint (0=False,-1=True)
Cadence	LineType	Track	vertical/horizontal/odd
Cadence	Justify	Des	Text Justification (CENTER,)
Cadence	IsMirror	Des	Text is mirrored (TRUE/FALSE)

Notes:

• units in mils
• Pin Type varies for each row (Pin/Net/PinX/PinY), but Component data is the duplicate info across these rows (x/y/L/R/T/B/Rotation/Layer).

References

• Grateful to the developers of the many software packages used throughout this project including, but not limited to, numpy, pytorch, pygeometric, pandas.
• https://github.com/pyg-team/pytorch_geometric
• https://pytorch-geometric.readthedocs.io/en/latest/notes/introduction.html
• https://colab.research.google.com/drive/14OvFnAXggxB8vM4e8vSURUp1TaKnovzX?usp=sharing#scrollTo=imGrKO5YH11-