Automated coverage convergence by correlating random variables with coverage variables sampled from simulation result data

US10831961B2 · kind B2 · utility

Assignee

• SYNOPSYS, INC. · US

Inventors

• Esha Dutta · Bengaluru, IN
• Danish Jawed · Savanuru, IN
• Bhaskar Pal · Bengaluru, IN
• Parijat Biswas · Savanuru, IN
• Pravash Chandra Dash · Bengaluru, IN
• Rajarshi Mukherjee · Savanuru, IN
• Sharad Gaur · Encrucijada, CU

Key dates

• Filing date: Jul 12, 2019
• Grant date: Nov 10, 2020
• Expiry date: Jul 12, 2039

Classification

• Technology area (CPC G): Physics
• CPC primary: G06F30/398
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

A data analysis engine is implemented in a testbench to improve coverage convergence during simulation of a device-under-validation (DUV). During a first simulation phase initial stimulus data is generated according to initial random variables based on user-provided constraint parameters. The data analysis engine then uses a time-based technique to match coverage variables sampled from simulation response data with corresponding initial random variables, determines a functional dependency (relationship) between the sampled coverage variables and corresponding initial random variables, then automatically generates revised constraint parameters based on the functional dependency. The revised constraint parameters are then used during a second simulation phase to generate focused random variables used to stimulate the DUV to reach additional coverage variables. In one embodiment, the functional dependency is determined by cross-correlating sampled coverage variables and corresponding initial random variables.