You will explore the evolution of Verilog and how SystemVerilog has expanded on its capabilities. You will learn about the key differences between Verilog and SystemVerilog, including new data types, enhanced design flexibility, and advanced verification techniques. The course also covers how SystemVerilog introduces powerful tools for verification, such as assertions, randomization, and functional coverage, to simplify and improve the design process. You should be familiar with the extensions introduced by SystemVerilog, its added features for both designers and verification engineers, and how these tools can be integrated into your design flow.

You’ll learn how to use SystemVerilog's string type, a dynamic, flexible way to handle and manipulate character data, making messages easier to create and manage. Time literals allow you to specify time delays with precision and clarity, improving readability and reliability in your code. Immediate assertions are covered to enable direct checks within your code, instantly identifying issues when specific conditions are met or failed. Enhancements to the fork…join construct will be explored, enabling more control over parallel tasks in verification setups. Additionally, you’ll review design features that have valuable applications in verification, including packages, structures, and arrays, making testbenches more adaptable and robust.

Learn how to use SystemVerilog’s new verification blocks to improve the organization and effectiveness of your testbenches. Understand how the SystemVerilog event scheduler divides time slots into distinct regions, providing better control over simulation actions. Use programs to separate verification code from design, reducing the risk of race conditions and helping to organize code more clearly. Explore final blocks, which run at the conclusion of a simulation, enabling you to collect summary statistics and ensure all tasks are completed before simulation ends. Lastly, learn how clocking blocks help separate timing from functionality by managing signal sampling and driving, making testbench creation more straightforward and cycle delays easier to handle.

Learn to use SystemVerilog’s randomization features to create both directed and random stimulus for verification. Understand how to apply pseudo-random number generators and seeding to control randomness, allowing you to generate large amounts of stimulus while still focusing on specific areas. Apply constraints to keep random values within legal limits and ensure meaningful test coverage for critical cases. Explore how randcase enables random selection of statements for execution, and how randsequence allows for the execution of a random sequence of statements, making stimulus generation efficient and adaptable for various testing scenarios.

Learn how to use SystemVerilog’s Object-Oriented Programming (OOP) features to create classes that enhance the flexibility and structure of test environments. Gain skills in defining properties and methods for data manipulation, and in using constructors to initialize objects during simulation. Understand the benefits of encapsulation to protect data, as well as inheritance and polymorphism to create reusable, layered data types. These features allow you to dynamically create and destroy objects, making your testbench more versatile and maintainable. Additionally, explore how static properties and methods enable shared data access across instances, enhancing test efficiency and code organization.

Learn to apply polymorphism and virtual methods in SystemVerilog to enhance testbench adaptability and modularity. Gain the ability to create virtual methods and virtual classes that allow base classes to be inherited and modified as needed, reducing the need to alter existing code. Understand how to cast and copy class instances, enabling flexible interaction between different class types. By using polymorphic behavior, you can make testbenches more reusable and easier to maintain, as well as customize them for various testing requirements without restructuring your code. These skills will help you build generic and extensible testbench frameworks.

Learn to use randomization to control class properties, which enables generating complex and diverse test stimuli efficiently. Apply pre- and post-randomize methods to manage specific steps before and after randomization to produce tailored stimuli for different testing needs. Adjust constraints on properties to create valid and targeted data within defined boundaries. Switch easily between different constraint settings to control random generation based on test requirements. Understand how to set random seeds to reproduce results consistently, allowing repeatable testing scenarios. Through these skills, create more dynamic and comprehensive stimulus patterns that help cover corner cases and improve test coverage.

Learn to implement data-oriented functional coverage using SystemVerilog covergroups, which enables defining what variables and events to sample. Set up coverage bins that count specific values and transitions, either automatically or explicitly. Use scalar and vector bins to monitor individual or combined value coverage, and define ignore_bins and illegal_bins to focus on valid cases. Understand cross-product coverage, including automatic and user-defined cross bins, to analyze interactions between different coverpoints. Gain control over the behavior of covergroups, coverpoints, and cross coverage, and use SystemVerilog functions to start, stop, and monitor coverage, and manage results for targeted verification.

Learn to use SystemVerilog arrays—dynamic arrays, associative arrays, and queues—to handle and organize complex test data efficiently. Understand the versatility of dynamic arrays for managing data that changes size during simulation and the flexibility of associative arrays for sparse data sets, ideal for modeling extended address spaces. Apply queues for data that requires strict ordering, supporting both FIFO and LIFO mechanisms. Use array methods, including locator, ordering, and reduction, to enhance data access and manipulation. With these tools, manage large data sets effectively to improve simulation quality and test coverage.