AI-driven anomaly detection in oscilloscope images for post-silicon validation

In the post-silicon validation process, different functionalities and boundaries of a system-on-chip (SoC) are tested, generating a large amount of data in the form of oscilloscope images, trace data, and log files. Oscilloscope images are used to visualize and analyze the digital I/O signals and play a crucial role in detecting anomalies. However, the debugging process of the oscilloscope images requires a lot of manual data analysis, which is time-consuming, inefficient, costly, and prone to errors. This paper proposes an artificial intelligence (AI) model to automatically detect anomalies in the oscilloscope images. Our proposed model uses a Convolutional Autoencoder (CAE), a neural network, which we train on real silicon data obtained from various post-silicon validation projects. While autoencoders have been used for anomaly detection, this is the first use to detect anomalies in oscilloscope images for post-silicon validation. Moreover, the state-of-the-art techniques use Reconstruction Error (RCE) as an anomaly detection metric, we show that a combination of RCE and Kernel Density Estimation (KDE) error metrics greatly reduces the false negatives (68%) for the anomalous category and improves the recall metric from 62% to 88%, making our approach 41% better. In addition, our proposed model achieves 99% precision in categorizing not anomalous data points. Furthermore, the proposed model has been deployed in the production environment, significantly reducing human effort.