Data Augmentation

A significant barrier in data-driven analysis, especially deep learning, is the lack of data. In microelectronics security, the development of pre-silicon assessment tools and post-silicon assistance tests requires lots of real-world test articles, benchmarks, measurements, and datasets. The obvious advantage of real examples is that they have security vulnerabilities already identified, e.g. CVEs in the National Vulnerability Database. However, such designs are typically confidential, proprietary, or difficult to share. Additionally, collecting images and/or measurements from real-world systems can be time consuming and expensive. This has led most researchers to rely on open-source data, which is also limited.

In one of FINS’s thrusts on this topic, we focus on generating arbitrarily large amounts of synthetic test articles and benchmarks using data augmentation–a technique used to increase the amount of data by adding slightly modified copies of already existing data. For example, in the image domain, we are employing Generative Adversarial Networks (GANs) and semantic maps to create realistic optical and Scanning Electron Microscope (SEM) images for counterfeit detection, hardware Trojan detection, etc. At the circuit level, FINS is creating diverse test articles and benchmarks using a mixture of parameter variation, traditional optimization, and AI-based optimization.