For a practical open-source digital-chip experiment, start with OpenROAD-flow-scripts (ORFS): Yosys synthesizes RTL into a netlist, and OpenROAD handles physical-design stages such as floorplanning, placement, clock-tree synthesis and routing. AI can assist with RTL edits, flow instructions or optimization proposals, but the EDA tools—not an AI explanation—must determine whether a design works and how its measured results change.
Which open-source tools cover an RTL-to-GDSII experiment?
EDA tools cover different parts of the chip-design process. A synthesis tool, a physical-design engine and a packaged flow are related, but they are not interchangeable. ORFS is a useful starting point when you want a repeatable digital flow and access to its intermediate stages.
| Tool or flow | Role | When it fits an experiment |
|---|---|---|
| OpenROAD | Physical-design engine with Tcl and Python control and a GUI. It is an extensible component, not an AI chip designer or a complete flow by itself. | When you need to control or inspect physical-design steps, or build a flow around the engine. |
| OpenROAD-flow-scripts (ORFS) | Reference RTL-to-GDSII flow. Its listed stages include Yosys synthesis, floorplanning, placement, clock-tree synthesis, routing, finishing, GDS generation, and DRC/LVS checks. Tcl and Python APIs allow manual intervention. | A strong choice for reproducible digital-flow experiments, provided you have RTL, constraints, platform files and a compatible PDK. |
| Yosys | Logic synthesis, including the synthesis stage used by ORFS. It does not perform physical place and route. | When an experiment concerns converting or optimizing RTL before physical design. |
| OpenLane | An automated RTL-to-GDSII flow combining OpenROAD, Yosys, Magic, Netgen, KLayout and other components. | Primarily for reproducing existing OpenLane projects or documented shuttle flows; the repository says the original flow is in maintenance mode. |
| Google XLS | A high-level synthesis toolchain for generating synthesizable designs from higher-level descriptions. | When the experiment starts above RTL. XLS does not replace the downstream physical-design flow. |
| Bazel Rules HDL | Build rules for hardware-description languages including Verilog, VHDL, Chisel and nMigen, using open tools such as Yosys, Verilator and OpenROAD. | When you need reproducible builds across a project using multiple hardware tools; it is not itself an EDA implementation engine. |
OpenROAD’s repository currently identifies Bazel as its supported build system and says CMake is deprecated. Check the repository’s current build guidance before installing, since build instructions and dependencies can change.
Where can AI help without taking the tools out of the loop?
“AI-assisted chip design” can mean several quite different things. A language model might draft or revise RTL, explain flow documentation, suggest configuration changes, or propose candidates for a measured design-space search. OpenROAD’s project describes infrastructure and directions including Python APIs, ML-friendly formats such as CircuitOps, reinforcement learning in the EDA loop, and LLM-guided multi-objective optimization. These are capabilities and areas of development, not a guarantee that an AI system will produce correct RTL or improve every design.
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| AI task | What to evaluate | What it does not establish |
|---|---|---|
| Draft or revise RTL | Whether the edited design passes simulation and the same synthesis and physical-design flow as the baseline. | A plausible explanation or syntactically valid RTL is not proof of functional correctness. |
| Answer flow and setup questions | Whether suggested commands and settings match the documentation and versions in your environment. | Documentation assistance does not show that the assistant can independently deliver signoff-ready silicon. |
| Propose optimization candidates | Whether measured area, timing and other relevant checks improve under unchanged, recorded conditions. | One successful result does not establish that the suggestion generalizes to other designs or platforms. |
Two research projects illustrate the difference between these roles. The 2025 MCP4EDA preprint describes an MCP server through which LLMs can orchestrate Yosys synthesis, Icarus Verilog simulation, OpenLane place and route, GTKWave analysis and KLayout visualization. Its authors report 15–30% timing-closure improvement and 10–20% area reduction versus default synthesis flows for representative digital designs in their evaluation. Those are the authors’ results for the tested designs and methodology, not expected gains for arbitrary projects. Read the MCP4EDA paper.
The 2024 ORAssistant preprint describes a retrieval-augmented conversational assistant over OpenROAD and related tool documentation. Its focus is help with setup, commands, flow configuration and execution: a documentation assistant, rather than evidence of autonomous chip implementation. Read the ORAssistant paper.
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How to run a controlled AI-assisted experiment
Keep the change small enough to attribute any result. For example, ask an assistant to revise one block of RTL or propose a bounded flow-setting change, then compare it with an unchanged baseline.
- Define the design and objective. Choose a small digital design and state what you want to improve, such as a timing or area metric, without relaxing the functional requirements.
- Record a baseline. Run the design’s ordinary simulation and the selected flow. Save the RTL, constraints, platform and PDK details, tool versions, scripts, and relevant reports.
- Make one bounded AI-proposed change. Keep the prompt, proposed edit and any configuration changes so you can identify what differed from the baseline.
- Check function before comparing physical results. Run simulation and synthesis, then continue through physical design only if the candidate meets the same functional checks as the baseline.
- Compare like with like. Use the same constraints, platform, PDK, tool versions and flow settings where possible. Compare the resulting reports and checks, not the assistant’s prediction.
- Keep the result reproducible. Preserve the candidate, scripts and reports together. If several changes were made, test them separately before attributing an improvement to one of them.
ORFS lists GDS generation and DRC/LVS checks among its flow stages, but a generated layout or passing flow checks alone should not be presented as proof that any particular design is ready for fabrication. The conclusion depends on the design, platform and requirements being evaluated.
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Can OpenROAD use an open PDK?
OpenROAD describes itself as PDK-independent, while noting that validation is conducted through flow controllers and specific PDKs. That distinction matters: an engine’s ability to model a platform is not the same as having public access to its process-design kit and platform files.
| Platform or PDK named by the OpenROAD repository | Qualification |
|---|---|
| SKY130 | Open PDK option; 130 nm. OpenLane also lists support for it. |
| GF180 | Open PDK option; 180 nm. OpenLane also lists support for it. |
| Nangate45 | Listed as an open platform option; 45 nm. |
| ASAP7 | Listed as a predictive platform; 7 nm. “Predictive” should not be confused with a production process kit. |
| GF12, Intel22, Intel16, TSMC65 and other proprietary configurations | The repository lists these configurations but says platform files and kits cannot be provided because of NDA restrictions. |
These platform details reflect statements in the OpenROAD repository accessed October 4, 2026. Check its current platform guidance, and the chosen flow’s compatibility, before committing to a project.
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Should you use OpenLane or LibreLane for a new design?
The OpenLane repository says the original OpenLane is in maintenance mode and recommends LibreLane for new designs. OpenLane remains relevant when reproducing an existing design or following a documented flow that uses it. For a new project, treat LibreLane as the successor named by that repository, then verify its current release, installation instructions and PDK support in its own documentation before relying on them. The successor notice alone does not establish a particular version or compatibility.
How mature is the open-source ecosystem?
Project-reported figures offer context, but they are not independent benchmarks of an AI-assisted workflow. The OpenROAD homepage reports “1000+ runs and completed chip designs” across technology nodes from 180 nm down to 12 nm and “500+ peer-reviewed research publications and conference papers”; it does not state a year for these figures. The OpenROAD repository separately reports over 600 silicon-ready tapeouts in SKY130 and GF180 through Google-sponsored Efabless MPW and ChipIgnite programs, also without a stated year. These are differently worded project figures and should not be treated as the same measure. See the OpenROAD Project homepage and its repository.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesFor learning the workflow, DTU hosts the textbook Introduction to Chip Design Using Open-Source Tools. Its availability as an educational reference does not establish that a particular edition is currently sold by a retailer.
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