Invention Grant
- Patent Title: Automated coverage convergence by correlating random variables with coverage variables sampled from simulation result data
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Application No.: US16510810Application Date: 2019-07-12
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Publication No.: US10831961B2Publication Date: 2020-11-10
- Inventor: Esha Dutta , Danish Jawed , Bhaskar Pal , Parijat Biswas , Pravash Chandra Dash , Rajarshi Mukherjee , Sharad Gaur
- Applicant: Synopsys, Inc.
- Applicant Address: US CA Mountain View
- Assignee: Synopsys, Inc.
- Current Assignee: Synopsys, Inc.
- Current Assignee Address: US CA Mountain View
- Agency: Bever, Hoffman & Harms, LLP
- Priority: com.zzzhc.datahub.patent.etl.us.BibliographicData$PriorityClaim@3b082300
- Main IPC: G06F30/367
- IPC: G06F30/367 ; G06F30/398

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.
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