A Deep Learning Approach in Optical Inspection to Detect Hidden Hardware Trojans and Secure Cybersecurity in Electronics Manufacturing Supply Chains

Abstract

Deep learning methods have been extensively studied and have been proven to be very useful in multiple fields of technology. This paper presents a deep learning approach to optically detect hidden hardware trojans in the manufacturing and assembly phase of printed circuit boards to secure electronic supply chains. Trojans can serve as backdoors of accessing on chip data, can potentially alter functioning and in some cases may even deny intended service of the chip. Apart from consumer electronics, printed circuit boards are used in mission critical applications like military and space equipment. Security compromise or data theft can have severe impact and thus demand research attention. The advantage of the proposed method is that it can be implemented in a manufacturing environment with limited training data. It can also provide better coverage in detection of hardware trojans over traditional methods. Image recognition algorithms need to have deeper penetration inside the training layers for recognizing physical variations of image patches. However, traditional network architectures often face vanishing gradient problem when the network layers are added. This hampers the overall accuracy of the network. To solve this a Residual network with multiple layers is used in this article. The ResNet34 algorithm can identify manufacturing tolerances and can differentiate between a manufacturing defect and a hardware trojan. The ResNet operates on the fundamental principle of learning from the residual of the output of preceding layer. In the degradation issue, it is observed that, a shallower network performs better than deeper network. However, this is with the downside of lower accuracy. Thus, a skip connection is made to provide an alternative path for the gradient to skip forward the training of few layers and add in multiple repeating blocks to achieve higher accuracy and lower training times. Implementation of this method can bolster automated optical inspection setup used to detect manufacturing variances on a printed circuit board. The results show a 98.5% accuracy in optically detecting trojans by this method and can help cut down redundancy of physically testing each board. The research results also provide a new consideration of hardware trojan benchmarking and its effect on optical detection.