A chip open/short circuit defects inspection method is proposed based on infrared images series. Firstly, the mean temperature series of the critical area of chip during the response process of the power-on procedure was recorded, the Savitzky Golay convolution smoothing method was applied to extract the time domain feature parameters after smoothing and filtering, and the principal component analysis method was uesd to select the key features. Then a support vector machine classification model was constructed, whose parameters were optimized by particle swarm algorithm to effectively distinguish different types of circuit board defects. In order to prove the validness of the method proposed, a variety of solder ball open/short circuit experiments were carried out on a CPU chip of circuit board. The research results show that the cross-validation classification accuracy of the optimized SVM model in the test dataset is 96.90%, which proves the validness of the method for detecting the chip open/short defects in this paper.