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

Patent metadata

Field	Value
Publication number	US-10831961-B2
Application number	US-201916510810-A
Country	US
Kind code	B2
Filing date	Jul 12, 2019
Priority date	Jul 14, 2018
Publication date	Nov 10, 2020
Grant date	Nov 10, 2020

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.

First claim

The invention claimed is:

1. In an electronic design automation (EDA) tool in a computer, a testbench configured to generate simulation data applied to a device-under-verification (DUV) during a simulation process and to receive simulation result data generated by said DUV in response to said applied simulation data, wherein said simulation result data includes a coverage point solution variable each time said simulation data causes the DUV to reach a corresponding coverage point operating state, said testbench including instructions stored on a computer-readable medium such that, when executed by a processor of said computer, cause the processor to perform operations comprising:
performing a first simulation process phase using a plurality of initial random variables generated in accordance with initial constraint parameters such that the DUV generates first simulation result data corresponding to the plurality of initial random variables;
identifying a corresponding first initial random variable using a time-based association process that correlates a time when the corresponding first initial random variable is used to perform the first simulation process phase with a sample time when a first coverage point solution variable is sampled from said first simulation result data;
generating one or more revised constraint parameters based on the identified first initial random variable; and
performing a second simulation process phase using a plurality of focused random variables generated in accordance with said one or more revised constraint parameters.

2. The testbench of claim 1 , wherein performing the first simulation process phase comprises utilizing a random variable generator to sequentially generate said plurality of initial random variables in accordance with said initial constraint parameters, utilizing a stimulus generator to generate first-phase stimulus data in accordance with said initial random variables, utilizing a simulator to apply said first-phase stimulus data to said DUV, and utilizing a data analyzer to monitor said first-phase simulation result data generated by said DUV in response to said applied first-phase simulation data.

3. The testbench of claim 2 , wherein identifying said corresponding first initial random variable comprises correlating a first time-stamp value corresponding to a time period in which said first coverage point solution variable was sampled with a second time-stamp value corresponding to a time period in which said corresponding first initial random variable was generated by said random variable generator.

4. The testbench of claim 3 ,
wherein the data analyzer includes a coverage monitor configured to sample said first coverage point solution variable from said first-phase simulation result data, and
wherein the testbench further comprises a data analysis engine configured to receive said sequentially generated plurality of initial random variables and a plurality of second time-stamp values from said random variable generator, configured to receive said sampled first coverage point solution variable and said first time-stamp value from said coverage monitor, and configured to identify said corresponding first initial random variable by matching said first time-stamp value with a corresponding second time-stamp value of said plurality of second time-stamp values that is associated with said first initial random variable.

5. The testbench of claim 4 , wherein said data analysis engine is further configured to cross-correlate said first coverage point solution variable and said corresponding initial random variable to identify functionally related pattern portions in said corresponding initial random variable, configured to utilize said functionally related pattern portions to generate said revised constraint parameters, and configured to transmit said revised constraint parameters to said random variable generator when said testbench performssaid second simulation process phase.

6. The testbench of claim 2 , wherein performing the second simulation phase comprises utilizing said random variable generator to generate said focused random variables in accordance with said revised constraint parameters, utilizing said stimulus generator to generate second-phase stimulus data in accordance with said focused random variables, and utilizing said simulator to apply said second-phase stimulus data to said DUV.

7. The testbench of claim 1 , wherein generating said one or more revised constraint parameters comprises:
cross-correlating said first coverage point solution variable and said corresponding initial random variable; and
utilizing results from said cross-correlating to generate said revised constraint parameters.

8. A computer implemented method for performing coverage verification of a device-under-verification (DUV) using a simulation process including applying simulation data to the DUV and receiving simulation result data generated by said DUV in response to said applied simulation data, wherein said simulation result data includes a coverage point solution variable each time said simulation data causes the DUV to reach a corresponding coverage point operating state, said method comprising:
performing a first simulation process phase using a plurality of initial random variables generated in accordance with initial constraint parameters such that the DUV generates first simulation result data corresponding to the plurality of initial random variables;
identifying a corresponding first initial random variable using a time-based association process that correlates a time when the corresponding first initial random variable is used to perform the first simulation process phase with a sample time when a first coverage point solution variable is sampled from said first simulation result data;
generating one or more revised constraint parameters using one or more functionally related pattern portions of the identified first initial random variable; and
performing a second simulation process phase using a plurality of focused random variables generated in accordance with said one or more revised constraint parameters.

9. The method of claim 8 , wherein performing the first simulation process phase comprises utilizing a random variable generator to sequentially generate said plurality of initial random variables in accordance with said initial constraint parameters, utilizing a stimulus generator to generate first-phase stimulus data in accordance with said initial random variables, utilizing a simulator to apply said first-phase stimulus data to said DUV, and utilizing a data analyzer to monitor said first-phase simulation result data generated by said DUV in response to said applied first-phase simulation data.

10. The method of claim 9 , wherein identifying said corresponding first initial random variable comprises correlating a first time-stamp value corresponding to a time period in which said first coverage point solution variable was sampled with a second time-stamp value corresponding to a time period in which said corresponding first initial random variable was generated by said random variable generator.

11. The method of claim 10 ,
wherein the data analyzer includes a coverage monitor configured to sample said first coverage point solution variable from said first-phase simulation result data, and
wherein the method further comprises matching said first time-stamp value with a second time-stamp value assigned to said first initial random variable.

12. The method of claim 11 , wherein the method further comprises:
cross-correlating said first coverage point solution variable and said corresponding initial random variable to ide

Assignees

• Synopsys Inc

Inventors

• Dutta Esha
• Jawed Danish
• Pal Bhaskar
• Biswas Parijat
• Dash Pravash Chandra
• Mukherjee Rajarshi
• Gaur Sharad

Classifications

• G06F30/3312 Primary

  Timing analysis · CPC title

• G06F30/367 Primary

  Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods · CPC title

• G06F30/398

  Design verification or optimisation, e.g. using design rule check [DRC], layout versus schematics [LVS] or finite element methods [FEM] (optical proximity correction [OPC] design processes G03F1/36) · CPC title