PathWave Waveform Analytics

Debug voltage and current spikes, IO glitches, and time shifts in pre-silicon using powerful machine learning

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Highlights

Designing and manufacturing low-power products that are robust, reliable, and secure requires the collection and analysis of extensive and long-duration waveform data from oscilloscopes and analyzers. Automate this extremely manual task and raise your engineering productivity without sacrificing accuracy.

Keysight's PathWave Waveform Analytics is an edge-to-cloud computing application that improves anomaly detection and reduces data storage costs in pre-silicon validation using machine learning algorithms.

The automated technology combines pre- and post-processing machine learning models that streamline anomaly discovery. By debugging in pre-silicon, you save time in the costly post-silicon validation phase, reducing your overall project costs. The software helps you detect waveform anomalies through a single dashboard that displays an unlimited number of channels. 

  • Compress long-duration waveforms by 60%
  • Reduce waveform transfer time by 10x
  • Easily transfer data using edge-to-cloud computing applications
  • View an unlimited number of channels on a single dashboard
  • Pin waveforms of interest for comparison analysis
  • Analyze any portion of the big vector data

Detect Anomalies Quickly Using Machine Learning

With its AI-based outlier detection and classification capabilities, PathWave Waveform Analytics enables R&D and design validation engineers to quickly detect anomalies such as voltage and current spikes on power and signal waveforms. 

Performing manual inspections of large waveform data sets is no longer necessary. Instead, you can run multiple validation tests, perform multiple dimensional comparisons, and fine-tune the waveforms over multiple channels, all using edge-to-cloud computing. You can also easily identify functional inconsistencies, rectify them quickly, and shorten costly validation time, thereby reducing overall budget costs.

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