Many electronic design automation (EDA) tools remain out of reach for semiconductor startups and research organizations. However, a growing number of open-source EDA solutions are bridging the affordability gap and democratizing advanced chip design. This article discusses how tools like OpenROAD and OpenLane lower entry barriers, reduce development costs, and drive innovation. It also explores how OpenROAD supports chip design powered by artificial intelligence (AI) and machine learning (ML) research within a complete design flow.
Barriers to chip design and verification
Research organizations and semiconductor startups face significant challenges in designing and taping new chips (Figure 1). These barriers include reliance on proprietary EDA tools, a shortage of skilled engineers, and high verification costs. Designers must also navigate increasingly complex monolithic system-on-chip (SoC) and emerging multi-die (chiplet) architectures while optimizing power, performance, and area (PPA).
AI and ML accelerators, targeting data centers and the intelligent edge, introduce additional design complexities. These AI chips span application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), neural processing units (NPUs), central processing units (CPUs), and graphics processing units (GPUs). Each accelerator demands specific PPA trade-offs, optimized integration of interconnects, on-chip and distributed memory, and workload scheduling mechanisms.
The U.S. government launched the $280 billion CHIPS for America Act in 2022 to address these challenges. Beyond the CHIPS Act, open-source EDA tools like OpenROAD empower engineers by democratizing advanced technologies, lowering development costs, driving innovation, and broadening accessibility.
Democratizing semiconductor design with OpenROAD
With contributions from over 120 GitHub developers and tens of thousands of updates to its codebase, OpenROAD reduces development costs for a wide range of chip designs. Adopted by startups, academic researchers, and established semiconductor companies, It has enabled over 600 chip tape-outs at process nodes ranging from 180nm to 12nm.
OpenROAD delivers a complete register-transfer level (RTL) to graphic data system (GDSII) design flow (Figure 2), simplifying and automating the major stages of digital chip design. The OpenROAD stack integrates open-source tools such as Yosys for RTL synthesis and TritonFPlan for floorplanning and power planning, which supports I/O pin placement and power delivery network (PDN) generation. TritonRoute, another core component of OpenROAD, manages detailed routing.
Optimized global and detailed placement workflows handle standard cells and macros, ensuring valid and efficient layouts. Additional steps — such as clock tree synthesis (CTS), routing, and physical verification — are managed by integrated tools like OpenSTA for timing analysis and OpenRCX for parasitic extraction. OpenROAD supports filler cell insertion, back-end-of-line (BEOL) fill, and seamless integration with external tools like Magic, Netgen, and KLayout. This comprehensive flow reduces design complexity while increasing flexibility.
OpenROAD supports many process design kits (PDKs), from open-source options like GF180 and SkyWater130 to proprietary advanced nodes such as TSMC65, GF12, Intel22, and Intel16. Its integration with the ASAP7 7nm Predictive PDK enables researchers and startup engineers to explore advanced 7nm FinFET designs. Additionally, OpenROAD facilitates architectural exploration for emerging algorithms and designs, particularly when detailed PPA data from prior projects is unavailable.
OpenLane: extending OpenROAD to optimize PPA tradeoffs
Efabless integrates OpenROAD into its chipIgnite prototyping platform and program. OpenLane provides an automated RTL-to-GDSII flow (Figure 3) built on OpenROAD, Yosys, Magic, Netgen, and custom methodology scripts for design exploration and optimization. Acting as a no-human-in-the-loop RTL-to-GDS compiler, OpenLane balances PPA trade-offs, enabling users to generate hardware representations without requiring extensive design experience. As part of the broader open-source EDA initiative, OpenLane extends OpenROAD’s capabilities for silicon implementation across academia, industry, and research.
OpenLane is available in two versions:
- OpenLane 1: built entirely on open-source software, OpenLane 1 is ideal for designs targeting Efabless multi-project wafer (MPW) programs, such as chipIgnite. Powered by Docker, it is best known for its simplicity and validated workflows.
- OpenLane 2: designed for advanced customizability, OpenLane 2 features a Python-based infrastructure that allows designers to define custom ASIC workflows using reusable components called Steps. Users can create decision-driven workflows, experiment in Python Notebooks, and integrate open-source and proprietary tools. Supporting projects like the Tiny Tapeout multiplexer, OpenLane 2 addresses the demands of diverse and complex designs.
Enabling AI-driven chip design and ML research
OpenROAD accelerates the open hardware and software movement by promoting transparency, collaboration, and accessibility in semiconductor design. By providing a free, open-source chip design tool, OpenROAD reduces reliance on proprietary solutions, empowering researchers, startups, and academic institutions to innovate and create differentiated silicon products. Its integration with popular open-source tools like Yosys and Magic highlights the growing adoption of open-source methodologies across semiconductor workflows.
OpenROAD also actively supports AI-driven chip design and ML research. Engineers can develop and validate algorithms across a complete design flow rather than isolated tools and processes, significantly expanding opportunities for experimentation and optimization. OpenROAD’s Python APIs integrate ML inference results into its database, creating a feedback loop between ML algorithms and EDA tools that continually refines workflows and results. NVIDIA uses OpenROAD’s Python APIs within its CircuitOps platform to model chip data as labeled property graphs, enabling generative AI solutions for EDA research.
Conclusion
Open-source EDA tools like OpenROAD and OpenLane are democratizing advanced chip design for semiconductor startups, research organizations, and academia. Integrating seamlessly with proprietary and open-source tools enables engineers to address complex monolithic SoC and emerging multi-die (chiplet) architectures — OpenROAD and OpenLane further support AI-driven chip design and ML research, driving innovation across the semiconductor ecosystem.
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References
Open-Source Semiconductor Chip Design Tool Celebrates Success, UC San Diego (UCSD)
Solution for Open-Source Chip Design on AWS, AWS
OpenLane, eFabless
About Us, The OpenROAD Project
The OpenROAD Project: Unleashing Hardware Innovation, UCSD
Efabless Announces the Release of the OpenLane 2 Development Platform, Transforming Custom Silicon Design Flows, eFabless
OpenROAD: The Journey So Far and Roadmap (Andrew Kahng), Fossi Foundation
OpenROAD – Key Milestones on the Road Towards Good PPA, OpenROAD Project
Strengthening the Foundations of IC Physical Design and MLEDA Research, UCSD
OpenROAD and CircuitOps: Infrastructure for ML EDA Research and Education, UCSD
Vidya A. Chhabria, Arizona State University
