Neural Network Based Prediction of PCB Glass Weave Induced Skew

Patent metadata

Field	Value
Publication number	US-2018113974-A1
Application number	US-201615299546-A
Country	US
Kind code	A1
Filing date	Oct 21, 2016
Priority date	Oct 21, 2016
Publication date	Apr 26, 2018
Grant date	—

Abstract

Mechanisms are provided for implementing a skew rate artificial neural network (ANN). The mechanisms generate a training dataset for training the skew rate ANN. The training dataset comprises a plurality of sets of data and each set of data corresponds to a particular set of printed circuit board (PCB) and communication channel characteristics. The mechanisms train the skew rate ANN based on the training dataset to generate a trained skew rate ANN. The mechanisms then receive an input dataset representing a set of PCB and communication channel characteristics for a PCB design. The trained skew rate ANN generates a predicted skew factor for the PCB design based on the input dataset. The predicted skew factor is then output to a PCB design tool to modify the PCB design based on the predicted skew factor.

First claim

What is claimed is:

1 . A method, in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a skew rate artificial neural network (ANN), the method comprising:
generating, by the data processing system, a training dataset for training the skew rate ANN, wherein the training dataset comprises a plurality of sets of data, and wherein each set of data corresponds to a particular set of printed circuit board (PCB) and communication channel characteristics; training, by the data processing system, the skew rate ANN based on the training dataset to generate a trained skew rate ANN; receiving, by the data processing system, an input dataset representing a set of PCB characteristics and communication channel characteristics for a PCB design and communication channel of the PCB design; generating, by the trained skew rate ANN executing on the data processing system, a predicted skew factor for the communication channel based on the input dataset; and outputting, by the data processing system, the predicted skew factor for the communication channel to a PCB design tool to modify the PCB design based on the predicted skew factor.

2 . The method of claim 1 , further comprising:
configuring, by the data processing system, the skew rate ANN to include an input layer having a plurality of input nodes, where each input node receives a different characteristic of the PCB design or communication channel; configuring, by the data processing system, the skew rate ANN to include a hidden layer having a plurality of hidden nodes, where each hidden node applies weight values to inputs from the input nodes of the input layer and combines the inputs to generate an output; and configuring, by the data processing system, the skew rate ANN to include an output layer having an output node, where the output node combines outputs from the hidden nodes to generate the predicted skew factor.

3 . The method of claim 1 , wherein the PCB characteristics comprise glass weave properties of a glass weave surrounding the communication channel.

4 . The method of claim 1 , further comprising:
modifying, by the PCB design tool, the PCB design based on the predicted skew factor for the communication channel by modifying a degree of rotation of a differential stripline pair of the communication channel relative to a glass weave of the PCB design to reduce skew in the communication channel to be within a design tolerance based on the predicted skew factor.

5 . The method of claim 1 , wherein outputting the predicted skew factor for the communication channel to a PCB design tool to modify the PCB design based on the predicted skew factor comprises outputting one or more design constraints.

6 . The method of claim 5 , wherein the one or more design constraints comprise a maximum wire length for the communication channel based on the predicted skew factor and a skew tolerance of the PCB design.

7 . The method of claim 1 , wherein the skew rate ANN is a two layer feedforward ANN implementing a Bayesian regularization training algorithm.

8 . The method of claim 1 , further comprising:
modifying, by the PCB design tool, the PCB design based on the predicted skew factor for the communication channel to reduce skew in the communication channel to be within a design tolerance based on the predicted skew factor.

9 . The method of claim 1 , further comprising:
modifying, by the PCB design tool, the PCB design based on the predicted skew factor for the communication channel at least by one of:
modifying a rotation of an orientation of wire traces of the communication channel relative to a glass weave;
modifying an orientation of the glass weave relative to the wire traces of the communication channel; or
modifying material properties of materials surrounding the communication channel in the PCB design.

10 . The method of claim 1 , wherein the communication channel is a differential stripline pair of a bus in the PCB design.

11 . A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to:
generate a training dataset for training a skew rate artificial neural network (ANN), wherein the training dataset comprises a plurality of sets of data, and wherein each set of data corresponds to a particular set of printed circuit board (PCB) and communication channel characteristics; train the skew rate ANN based on the training dataset to generate a trained skew rate ANN; receive an input dataset representing a set of PCB characteristics and communication channel characteristics for a PCB design and communication channel of the PCB design; generate, by the trained skew rate ANN, a predicted skew factor for the communication channel based on the input dataset; and output the predicted skew factor for the communication channel to a PCB design tool to modify the PCB design based on the predicted skew factor.

12 . The computer program product of claim 11 , wherein the computer readable program further causes the computing device to:
configure the skew rate ANN to include an input layer having a plurality of input nodes, where each input node receives a different characteristic of the PCB design or communication channel; configure the skew rate ANN to include a hidden layer having a plurality of hidden nodes, where each hidden node applies weight values to inputs from the input nodes of the input layer and combines the inputs to generate an output; and configure the skew rate ANN to include an output layer having an output node, where the output node combines outputs from the hidden nodes to generate the predicted skew factor.

13 . The computer program product of claim 11 , wherein the PCB characteristics comprise glass weave properties of a glass weave surrounding the communication channel.

14 . The computer program product of claim 11 , wherein the computer readable program further causes the computing device to:
modify, by the PCB design tool, the PCB design based on the predicted skew factor for the communication channel by modifying a degree of rotation of a differential stripline pair of the communication channel relative to a glass weave of the PCB design to reduce skew in the communication channel to be within a design tolerance based on the predicted skew factor.

15 . The computer program product of claim 11 , wherein the computer readable program further causes the computing device to output the predicted skew factor for the communication channel to a PCB design tool to modify the PCB design based on the predicted skew factor at least by outputting one or more design constraints.

16 . The computer program product of claim 15 , wherein the one or more design constraints comprise a maximum wire length for the communication channel based on the predicted skew factor and a skew tolerance of the PCB design.

17 . The computer program product of claim 11 , wherein the skew rate ANN is a two layer feedforward ANN implementing a Bayesian regularization training algorithm.

18 . The computer program product of claim 11 , wherein the computer readable program further causes the computing device to:
modify, by the PCB design tool, the PCB design based on the predicted skew factor for the communication channel to reduce skew in the communicat

Assignees

• IBM

Inventors

• Boday Dylan J
• Chen Zhaoqing
• Hejase Jose A
• Krabbenhoft Roger S
• Roy Paladhi Pavel
• Tang Junyan

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06F30/20

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• G06N3/084 Primary

  Backpropagation, e.g. using gradient descent · CPC title

• G06F2111/04

  Constraint-based CAD · CPC title

• G06F30/394

  Routing (G06F30/396 takes precedence) · CPC title