US 12,223,248 B1
System, method, and computer program product for optimization-based printed circuit board design
Joydeep Mitra, Austin, TX (US); John Robert Murphy, Boston, MA (US); Zachary Joseph Zumbo, Brookline, MA (US); Luke Roberto, Brookline, MA (US); and Taylor Elsom Hogan, Boston, MA (US)
Assigned to Cadence Design Systems, Inc., San Jose, CA (US)
Filed by Cadence Design Systems, Inc., San Jose, CA (US)
Filed on Aug. 10, 2020, as Appl. No. 16/989,058.
Int. Cl. G06N 3/088 (2023.01); G06F 30/31 (2020.01); G06F 30/392 (2020.01); G06F 30/394 (2020.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06N 3/082 (2023.01); G06N 3/084 (2023.01); G06N 3/126 (2023.01); G06N 20/20 (2019.01); G06F 111/06 (2020.01); G06F 115/12 (2020.01)
CPC G06F 30/392 (2020.01) [G06F 30/31 (2020.01); G06F 30/394 (2020.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 3/126 (2013.01); G06F 2111/06 (2020.01); G06F 2115/12 (2020.01)] 12 Claims
OG exemplary drawing
 
1. A computer implemented method associated with a printed circuit board (“PCB”) design, comprising:
generating, using a processor, one or more placed PCB designs using a genetic optimization methodology including a reward function;
adjusting the one or more placed PCB designs and the reward function during the generating;
routing the one or more placed PCB designs using an auto-router to assign a routability score label;
training a neural network architecture, using the one or more placed PCB designs and the routability score label, to extract one or more intermediate features from the one or more placed PCB designs, wherein the neural network architecture includes:
a convolutional neural network (“CNN”) that processes image data representations of the one or more PCB designs,
a graph convolutional network (“GCN”) that processes graph data representations of the one or more PCB designs,
a first deep neural network (“DNN”) that processes scalar data representations of the one or more PCB designs, and
a second DNN that extracts the one or more intermediate features from outputs of the CNN, the GCN, and the first DNN; and
predicting a routability of the PCB design using the second DNN based upon, at least in part, the one or more intermediate features, wherein routability relates to a percentage of successfully routed connections between components of the PCB design.