少量データにおける畳み込みニューラルネットワークを用いた基板そり量変位の予測

Abstract

A convolutional neural network, which reproduce a function by data, was used to predict the amount of warp distortion of a four-layer circuit board in a reflow soldering process. The data used for training are material properties such as Young's modulus, board thickness, and residual copper content as input data, while the warpage strain data to be predicted is the amount of warp of the circuit board obtained from the measurement. Since several distortion data to be predicted was insufficient to be used for training, a newly proposed data augmentation method used to increase a total amount of data. Proposed data augmentation is evaluated through the result of the predictions and discussed.