Software solution speeds semiconductor design validation
An edge-to-cloud software solution that improves anomaly detection and reduces data storage costs in pre-silicon validation using machine learning algorithms has been launched by Keysight Technologies.
The automotive, IoT and mobile devices markets are growing rapidly. These markets need to leverage design thinking and innovative technology to enable semiconductor design engineers to quickly develop products that are robust, reliable and secure against malicious intrusions while reducing power consumption.
Keysight's PathWave Waveform Analytics is an advanced analytics software solution that includes a new data compression technology that enables long-duration waveform compression, high resolution playback and analysis exceeding several terabytes of data. Built-in machine learning improves the discovery of voltage and current anomalies, as well as transient trends captured by the waveforms.
Keysight's PathWave Waveform Analytics addresses challenges currently faced by semiconductor designers.
Key features and benefits are shorter analysis time in pre-silicon validation with patented machine learning algorithms that identify anomalies and outliers and a reduction of overall project costs by debugging in pre-silicon, which saves time in the costly post-silicon validation phase.
The software also improves design reliability with pre-and post-processing algorithms that accurately detect voltage and current spikes on power and signal waveforms
"Highly power-efficient semiconductors require robust, reliable and secure analytics during design qualification," said Christopher Cain, vice president and general manager of Keysight's Electronic Industrial Products. "Keysight's innovative big-data waveform analytics solutions enable those semiconductor designers to automate design analysis, improving productivity of those tasks by up to 90 percent, thus accelerating their companies' time-to-market opportunity."
Keysight's PathWave Waveform Analytics also enables designers to identify outlier waveform shapes via high level view into clustering results and analyse data in high resolution with hierarchical clustering for multi-level drill down.
It can query and analyse any portion of the big vector data or waveform that was captured and stored and respond quickly as minimum data is transferred between the edge computer and the server.
The user can view an unlimited number of channels on a single dashboard, with options to pin or move the waveform in the interested channel for comparison and perform multiple dimensional comparisons and fine-tune the waveforms for analysis