TUHH Open Research
Help
  • Log In
    New user? Click here to register.Have you forgotten your password?
  • English
  • Deutsch
  • Communities & Collections
  • Publications
  • Research Data
  • People
  • Institutions
  • Projects
  • Statistics
  1. Home
  2. TUHH
  3. Publications
  4. AI-driven anomaly detection in oscilloscope images for post-silicon validation
 
Options

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

Citation Link: https://doi.org/10.15480/882.13719
Publikationstyp
Conference Paper
Date Issued
2025-01
Sprache
English
Author(s)
Akash, Kowshic A.
Wulf, Tobias
Valentin, Torsten
Geist, Alexander
Kulau, Ulf  
Smart Sensors E-EXK3  
Lal, Sohan  
Massively Parallel Systems E-EXK5  
TORE-DOI
10.15480/882.13719
TORE-URI
https://hdl.handle.net/11420/52090
Citation
38th International Conference on VLSI Design and 24th International Conference on Embedded Systems (VLSID, 2025)
Contribution to Conference
38th International Conference on VLSI Design and 24th International Conference on Embedded Systems (VLSID, 2025)  
Scopus ID
2-s2.0-105000165020
Peer Reviewed
true
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.
Subjects
Post-Silicon Validation
Artificial Intelligence
Anomaly Detection
Convolutional Autoencoders
MLE@TUHH
DDC Class
004: Computer Sciences
621.3: Electrical Engineering, Electronic Engineering
Publication version
acceptedVersion
Lizenz
http://rightsstatements.org/vocab/InC/1.0/
Loading...
Thumbnail Image
Name

ml_post_silicon_validation.pdf

Size

2.33 MB

Format

Adobe PDF

TUHH
Weiterführende Links
  • Contact
  • Send Feedback
  • Cookie settings
  • Privacy policy
  • Impress
DSpace Software

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science
Design by effective webwork GmbH

  • Deutsche NationalbibliothekDeutsche Nationalbibliothek
  • ORCiD Member OrganizationORCiD Member Organization
  • DataCiteDataCite
  • Re3DataRe3Data
  • OpenDOAROpenDOAR
  • OpenAireOpenAire
  • BASE Bielefeld Academic Search EngineBASE Bielefeld Academic Search Engine
Feedback