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FPGA 03/13 · Testbench Claude — Self-Checking Tests
Builds machine-verifiable, file-driven HDL testbenches with independent golden references and negative controls.
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See it in action
You say
Generate a self-checking Verilog testbench for a 16-bit pipelined multiplier. Use external hex files for vectors and include a monitor task that counts errors against a golden model.
Your agent does
I have generated tb_multiplier.v. It features decoupled stimulus and monitor tasks, uses $readmemh to load tb/multiplier_vectors.hex, and performs comparisons at the end of the time step. A negative control pass verified the monitor by injecting a bit-flip in the DUT instantiation.
What you get
About this skill
The problem
Manual waveform inspection is slow and misses silent errors like single-cycle timing shifts. Without independent reference models, developers often build testbenches that share the same logic bugs as their RTL, leading to false passes that fail after synthesis.
What it does
- Generates self-checking HDL testbenches with machine-readable PASS/FAIL verdicts.
- Separates clock, reset, stimulus, and monitor tasks into independent processes to prevent race conditions.
- Implements file-driven verification using external .hex and .json vectors for reconfigurability without recompilation.
- Executes negative control tests against deliberately broken DUT variants to validate the checker's effectiveness.
- Applies biased random generation and exhaustive testing for small bit-widths to hit difficult corner cases.
Frameworks & tools
Verilog, SystemVerilog, HDL simulators (Icarus, Vivado, ModelSim), Python-based golden models.
Why this beats prompting it yourself
Standard LLM prompts often produce "first-fail" testbenches that lack proper timing synchronization or independent oracles. This skill enforces a strict verification architecture, including delta-cycle settled comparisons and mandatory negative controls, ensuring your test suite actually catches bugs instead of just printing signals.
Use cases
- Verifying complex RTL modules against a Python-based golden reference.
- Building regression suites that load test vectors from external files.
- Stress-testing arithmetic units with biased random stimulus for boundary values.
- Proving testbench reliability via fault-injection and negative control runs.
Known limitations
Requires an existing golden model or oracle definition to establish independence. Optimized for Verilog/SystemVerilog workflows.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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