The OpenROAD Project: Difference between revisions

A major open-source project, the OpenROAD Project (Open Realization of Autonomous Design), aims to provide a fully automated, end-to-end digital integrated circuit design flow (RTL-to-GDSII), thereby eliminating the need for human intervention. OpenROAD was started in 2018 to address the high cost, inexperience, and unpredictability of conventional EDA tools as part of DARPA's IDEA initiative. It achieves this by enabling a 24-hour, no-human-in-loop (NHIL) flow that matches the usual quality of design and produces layouts suitable for GDSII. Leading a cooperation under a permissive BSD license, UC San Diego keeps OpenROAD available. Among the business partners are Arm, Qualcomm, SkyWater, and others. Among its main features are scripting interfaces (Tcl/Python) and a common database (OpenDB), which help designers automate or personalize every phase of the digital design process. The project aims to democratize hardware design and promote rapid innovation in integrated circuit (IC) design by reducing barriers related to cost, time, and experience.Projects using the flow range from Hammer at the University of California, Berkeley, to the FASoC analog/mixed-signal flow to the Zero-ASIC Silicon Compiler. Readymade open ASIC flows, including OpenLane and OpenROAD flow scripts, are based on this.

History and Motivation

Modern digital integrated circuit design is a complex, multi-stage process that requires specialized tools and professional tuning from expensive, proprietary sources. DARPA's IDEA program initiated OpenROAD, an autonomous, open-source RTL-to-GDSII flow designed to address the "design cost crisis" by eliminating the need for expert tinkering and licensing, thereby democratizing chip design and enabling smaller companies, research groups, and academic institutions to produce their semiconductor layouts. To achieve underperformance, power, and area (PPA) equivalent to commercial design processes, the aim was to create a no-human-in-loop flow that could take an RTL description and generate a GDSII mask-ready layout in under 24 hours.

Supported by partners Arm, Google, SkyWater, and others, OpenROAD was led by UC San Diego staff. The first iteration (v1.0, 2020) of a current FinFET technology (GF12LP, ~12 nm) produced a complete, integrated flow producing DRC-clean layouts. Along with an almost complete technology-node enhancement in PPA, Version 2.0 (2021) includes advanced capabilities (RC extraction, chip-package co-design). Hundreds of designs on the open SkyWater 130 nm PDK (complete with a Google MPW shuttle) and experimental runs on Intel 22 nm FinFET by 2021 have helped the community hasten the flow over time. Forming the foundation of the OpenLane and ChipIgnite projects, the open-source ecosystem for RISC-V System-on-Chip (SoC) designs has expanded rapidly and is now considered the leading open-source physical design infrastructure for digital integrated circuits. Through university courses and events such as the 7nm OpenROAD Design Challenge, which aims to increase the user base, the initiative aggressively promotes worker education and training.

Design philosophy and architectural technique

The keystones of the OpenROAD design philosophy are openness and automation. Its architecture is built on a shared in-memory design database and modular engines, each of which runs a step of the flow. Created by Athena Design Systems and turned open-source for this project, all of the tools share a common OpenDB data model and transmit data over standard LEF/DEF (and its binary variants). For RTL-to-GDS, designers can employ an autonomous OpenROAD flow script (ORFS) pipeline, or, for extra control, they can call certain stages using Tcl/Python commands driven by scripts. OpenROAD is, therefore, not just a reference autonomous flow but also a versatile platform for customized flows or research.

Fundamental ideas consist of the following:

• No-Human-In-Loop (NHIL): The instruments are designed to run free from human direction. OpenROAD's AutoTuner and machine-learning architecture, for example, methodically explores tool settings in the cloud, thereby reducing the need for expert hand-tuning.

• Integrated Data Model: All tools save the design in an Open Database (OpenDB). Rich in net connection, layout geometry, timing data, etc., OpenDB is hierarchical (it allows any cell hierarchy) and compatible with LEF/DEF. This means that any step can query or modify the chip data without incurring the expense of file I/O. For example, placement results can be transmitted immediately to CTS, or in-memory parasitics from routing can be reported back to STA.

• Extensibility and Scripting: Every OpenROAD component offers a Tcl command, such as clock_tree_synthesis, location, and route, that embodies scripting and extension. Furthermore, a Python API is currently under development. This allows users to integrate OpenROAD into more general tool flows or construct their design scripts. Designers might even use the API to handle difficult chores such as symmetric placements or customized power grid generation.

• Continuous Integration and Quality: OpenROAD utilizes Jenkins on Google Cloud to maintain a rigorous Continuous Integration (CI) pipeline, thereby ensuring resiliency. New designs, including genuine MPW projects and code analysis tools (like Coverity and Sanitizers), check for mistakes and often find use in regression tests. This focus on automation and testing enables early error identification and stability across a wide range of user designs.

• Multi-Node Support: OpenROAD is technology-agnostic, supporting multiple nodes. Verification on a range of PDKs, including GF 12 nm, predictive 7 nm (ASAP7), SkyWater 130 nm, and GF 65 nm (CLN65LP), has confirmed When a user supplies LEF/DEF or GDSII libraries for the target technology, that is, using the required pin resistances for timing and layer capacities for routing, OpenROAD automatically changes its flow. Consequently, it can be applied to both older processes and complex nodes.

The OpenROAD design forms a single EDA platform, as all essential RTL-to-GDSII processes are carried out by modules sharing a common database. Its open design lets new methods (ML-based tuning, GPU acceleration, etc.) be quickly incorporated and supports research and teaching, even as it continually optimizes to match industry power-to-performance (PPA) ratios.

Internal Database (OpenDB)

OpenROAD rests on OpenDB, the shared in-memory design database. First developed by Athena Design Systems and released under a BSD license in 2019, OpenDB aims to support this initiative. It is tightly based on the IEEE LEF/DEF standard, which is now supported by LEF/DEF 5.6 and includes improvements for suitable storage. OpenDB has the main qualities:

• Complete Physical Netlist Model: OpenDB may display the entire architecture, including obstacles, wires (routing geometries), pins, instances (with geometric locations), cell master definitions, and netlists. Thanks to its support of N levels of hierarchy, IP blocks and submodules may be stored and referenced.

• Properties and Attributes: It offers a method for adding custom data to objects using sparse or dense attributes. This lets developers add more data, such as time slabs or congestion scores, to nets or cells without having to rewrite the database entirely.

• Binary Format and LEF/DEF I/O: OpenDB has an in-built LEF/DEF parser. Designs are often entered into OpenDB from LEF/DEF format. OpenDB may also reload designs far quicker than textual LEF/DEF formats by supporting a proprietary binary save form for speed. This is crucial for an autonomous process that can iterate quickly.

• Quick Queries: OpenDB is designed to quickly answer frequently asked EDA questions. With indexed data structures, that is, for searching nets by name, objects by a bounding box, etc., it may store geometry (such as wires and barriers) compactly. Global routers and congestion analyzers, among other tools, utilize these searches to evaluate the layout efficiently.

• Signoff Exports: In addition to reviewing the final design, OpenDB enables you to document it. It can produce DEF as well as even GDSII for layout signoff. OpenROAD generates a GDSII using OpenDB to run outside DRC/LVS tools on the final layout.

Every OpenROAD tool operates from a single OpenDB instance. For example, floorplanning sets the cells there; placement changes those positions; clock-tree tools add buffers to OpenDB; routing adds wire geometry; and timing analysis obtains parasitics from OpenDB. The netlist and cell library data are placed in OpenDB following logic synthesis. This shared database approach removes the cost of format translation between stages and allows tight integration, that is, incremental changes and debugging.

RTL-to-GDSII Flow

Open ROAD follows the usual steps of an ASIC backend. Every stage is carried out using an open tool and interfaces use standard data formats (LEF/DEF, Liberty, SDC). Flow can be considered as follows:

1. Logic Synthesis: An RTL description (in Verilog) is first converted into a gate-level netlist using a logic synthesis tool. OpenROAD lacks its synthesizer and instead utilizes existing tools. Most users synthesize the open-source Yosys using it. Reading the RTL and a target cell library ( LIBerty.lib files), Yosys produces a flattened Verilog netlist. OpenDB loads the netlist, which includes timing arcs, into memory.

2. Floorplanning and Macro Placement: Physical planning begins once the netlist is established, involving the distribution of complex macros, pre-made blocks such as memory arrays, DSPs, and I/O pads, across the chip. Here, OpenROAD provides two complementary approaches:

• Macro Placer (TritonMacroPlacer): This utility automatically positions large macro instances on the die. TritonMacroPlacer adheres to design constraints (such as aspect ratio and keep-out sections) while exploring numerous macro configurations within the floorplan using an annealing-based solution, the ParquetFP algorithm. It follows limits such as halo surrounding macros and channel spacing. The method searches by "flipping" macro sites inside clusters to cut cable length or congestion.

• RTL-aware Clustering (RTL-MP): Designed to arrange logic according to the RTL or dataflow hierarchy, this new OpenROAD tool generates clusters. Every cluster is then managed as a "macro" that has to be positioned. With this hierarchy-driven approach, the designer has greater freedom to develop designs that resemble those produced hand-crafted. RTL-MP was demonstrated to arrange macros in a 12nm RISC-V SoC (BlackParrot) in a manner that minimized wire length and reduced critical paths, thereby producing quality equivalent to bespoke floorplanning.

Floorplanning helps determine the original chip form, macro coordinates, and power ring zones. The result of the following stage is recorded, for instance, as a DEF floor plan.

3. Standard-Cell Placement: After the macros have been used, the hundreds of fundamental logic gates, standard cells, are arranged in the remaining area. The analytical placement engine used globally by OpenROAD is RePlAce. Under RePlAce's concept of placement, a continuous optimization, each cell is treated as a charged particle. Based on Nesterov's accelerated gradient descent, a nonlinear solution distributes cells to avoid overlaps and shifts them to lower wire lengths. RePlAce dynamically changes sizes and offers a novel function that locally smoothes overlapping cell regions to promote convergence. One gets high-quality placement with strong profitability and a low half-perimeter wire length (HPWL). RePlAce runs in time-driven mode, guiding placement with congestion estimates from global routing and slack information from static timing analysis (OpenSTA). For example, OpenSTA is used in OpenROAD's placement to sustain fast vital networks, and FastRoute, a worldwide router, calls to monitor traffic. After global placement, a legalization step clamps cells to the fixed-row structure. The open database is then loaded with the final cell coordinates.

4. Clock-Tree Synthesis (CTS): One must build the clock network once placement is complete. At this point, OpenROAD uses TritonCTS 2.0. Using the target clock nets and the placed cells, TritonCTS automatically generates a buffered clock tree driving every clock pin. Since it characterizes buffers and wires in real time, no pre-computed library data is needed. Typically, building a balanced binary tree involves combining sinks, adding buffers at branch points, and routing clock lines, Which helps lower skew and insertion latency. TritonCTS automatically seeks a clock network with zero skew and minimum latency, even if users may use Tcl commands to change fundamental parameters (such as max slew and load capacitance). The generated clock pins and buffers are entered into OpenDB. (Although TritonCTS provides a basic solution straight out of the box, scripting allows further unique clock-tree upgrades such as H-trees or buffered meshes.)

5. Global Routing: Before detailed routing, a global router finds approximate paths for every net on a coarse grid, openROAD routes globally using the open-source FastRoute engine. Fast Route 4.1 (from Iowa State) is included in OpenDB using layer-by-layer netting that forecasts congestion based on the set layout (cells and macros). It arranges nets based on available resources and treats each metal layer as a grid of rails. Built to run in placement loops, the lightning-fast FastRoute generates a global routing graph, or collection of "routing guides," specifying which tracks and layer nets will be routed. This data helps identify any hotspots for routing congestion. The global routing result is stored in OpenDB for use in placement (congestion feedback) and detailed router guide inputs.

6. Detailed Routing: Using the global route standards as input, the comprehensive router allocates exact tracks and vias to link all nets. OpenROAD's complete router is TritonRoute. The first phases in TritonRoute's multi-stage procedure are pin access, which ties cell pins to routing tracks, and track assignment for every net. It then follows recurrent search and repair processes after an initial routing run depending on a labyrinth. Like variants of the A* or Lee algorithms, the "search and repair" phase is a conflict-driven process in which congested cables are split and redirected using additional pins or space. Most importantly, Triton Route enforces design constraints (minimum spacing, width, and via rules) using an integrated DRC engine, even while routing. The aim is to build a DRC-clean layout, in which case the metal/wire geometry conforms to all design guidelines. OpenDB includes all metal segments and via points, therefore reflecting the whole routed design. TritonRoute provides, via OpenDB, the whole routed geometry in DEF and GDS formats.

7. Timing Closure and ECO: Verification of temporal restrictions is ensured by following a routing approach, which is facilitated through timing analysis. OpenROAD analyses static timing with OpenSTA. Usually derived from SPEF, OpenSTA estimates arrival times and slacks by reading the parasitic delays and post-route netlist. If the timing is not closed, OpenROAD may enter an ECO (Engineering Change Order) cycle, especially in cases of hold-time violations in the latter phases. This mode fixes time by choosing between cell resizing or additional buffer insertions. OpenROAD, for example, starts an iterative ECO step automatically and should hold violations continue after CTS. This stage involves repeating the CTS/timing procedure, adding buffers along important nets, and so on until the holds are addressed. This ECO process is seen here: any hold violations that remain following the normal flow activate subsequent buffer-placing passes until no more violations are found. Timing is much improved by these iterations; one OpenTitan design required five ECO iterations to fix approximately 1,500 hold violations (see table). OpenSTA guarantees that the last time conforms to the constraints everywhere. Many processes start with automated ECO; upon scheduled closure, placement refinement or logic optimization can also be applied.

8. DRC/LVS Signoff: When routing is complete, and timing is finalized, the design is ready for signoff. Open ROAD exposes the design to open-source or commercial signoff tools rather than proprietary DRC/LVS rule checkers. For typical rulesets, the exported layout should be DRC-clean; however, TritonRoute's built-in DRC engine guarantees that detailed routing follows all layer-specific design requirements. An external LVS tool for LVS (layout-versus-schematic) would be used to generate the netlist and GDSII. OpenROAD is mostly concerned with layout development; final verification is expected from normal signoff procedures.

9. GDSII Export: Lastly, OpenROAD exports the completed layout from OpenDB to GDSII (mask format). Using the GDS writer of the OpenDB library, a GDSII stream may be generated for tape-out following every geometry's positioning and routing. Furthermore, if required, the DEF and LEF forms are created. Completing the RTL-to-GDSII pipeline generates a full chip layout fit for manufacturing.

At every stage, OpenROAD provides comprehensive reports and hooks. Users can search for cell density, timing data (via OpenSTA), congestion maps, and other relevant information. Alternatively, execute in headless mode or examine the flow using the OpenROAD App GUI. Because OpenROAD uses common design formats (Verilog, SDC, Liberty, and LEF/DEF) at its inputs and outputs, it can interface with many tools. A run of OpenROAD-flow-scripts may, for example, call write_gds at the end and Yosys (read_verilog, synth) at the beginning. Every phase of the operation can be automated or paused at any moment for inspection and review.

Core Tools and Algorithms

Every one of the specialist tools used in OpenROAD implements one stage of the flow. Important instruments include:

• OpenSTA: A gate-level static time analysis engine. Once OpenSTA has read the synthesized netlist, from LEF/DEF, Liberty timing libraries, SDC constraints, and parasitic SPEF, STA is performed. It details slights, delays, and key paths. Designed to be multi-threaded, OpenSTA has a strong connection with OpenDB. Since it employs industry-standard criteria, it can be regarded as a replacement for commercial time verifiers such as PrimeTime. It also drives in-place timing-aware placement.

• RePlAce (Global Placement): It is an open-source worldwide placement tool applying analytical (electrostatic) methods. Modeling cells as charged particles replaces Nesterov's accelerated gradient descent to minimize a weighted total of half-perimeter wire length and density penalties. Two major RePlAce innovations that improve the speed and quality of convergence are constraint-oriented local smoothing and adaptive step scaling (Cheng et al., 2018). The result is high-quality placement; RePlAce improved HPWL in benchmark comparisons over past placers. RePlAce runs in "mixed-size" mode in the OpenROAD flow and is optimized for timing-driven placement, thereby supporting both standard cells and macros.

• TritonMacroPlacer: Macro engine for hard block placement Based on the ParquetFP approach, which manages macro placement as a floorplan concern, TritonMacroPlacer is Using simulated annealing, that is, macro-exchanging, TritonMacroPlacer lowers wire length and conforms with meta-constraints (such as keep-out zones), therefore respecting a beginning floorplan (from RePlAce or RTL-MP). Placing the macros generates a final DEF. Unlike a flat approach, TritonMacroPlacer was used to automatically insert macros in an RTL-MP, 12nm design that had been clustered, thereby producing a layout that improved Fmax and reduced wire length.

• FastRoute (Global Router): Congestion influences a globally distributed router. First created by Andreescu et al., Fast Route quickly generates a worldwide routing plan on a coarse grid. The OpenROAD distribution is primarily composed of FastRoute 4.1 (developed at Iowa State). FastRoute reads the installed chip (via OpenDB) and then distributes each net across metal layers to routes, alleviating congestion. It creates congestion projections or capacity consumption maps to help with location avoidance. FastRoute paths on a grid instruct the detailed router; it does not undertake extensive routing.

• TritonCTS (Clock Tree Synthesizer): It is an open-source clock-tree builder. TritonCTS 2.0 (by Mateus Fogaça et al.) creates a buffered clock network for any clock domain. It utilizes on-the-fly characterization of buffers and wires, allowing it to replicate delay without relying on pre-characterized tables. Through recursively grouping sink pins and adding buffers, TritonCTS maximizes for the lowest skew. One may invoke it using the Tcl command clock_tree_synthesis. Unlike other commercial CTS engines, TritonCTS is entirely open and scriptable. Users could change load or maximum slew values or even create custom buffer lists for characterization purposes. TritonCTS creates balanced H-trees or hybrid-tree designs automatically.

• TritonRoute (Detailed Router): To finish the physical route, UCSD developed the thorough routing engine TritonRoute (Detailed Router). TritonRoute creates final wire segments and vias using the global route guide according to design rules. Its components include modules for initial labyrinth routing, track assignment (reserving wire tracks for nets), pin access (connecting each cell pin to the routing grid), and iterative repair. Like a Lee/A*-based routing system with rip-up-and-reroute, the repair process repeatedly tears up congested or misrouted nets and reroutes them with adjustments. TritonRoute has a thorough DRC checker to ensure rule compliance. Built for modern large designs, the algorithm supports ISPD-2018/2019 contest formats. It presently offers block-level (standard-cell + macro) routing for systems like GF65 (CLN65LP).

• AutoTuner: hyperparameter tuning framework Using machine learning approaches, OpenROAD's AutoTuner maximizes the many flow parameters (including those controlling placement and routing engines). Based on Ray, a distributed execution system, massive batches of tests may be conducted concurrently in the cloud. Once designers provide changeable parameters in a JSON setup, AutoTuner explores the parameter space to improve PPA. AutoTuner, for example, may generate many times higher speedups or PPA increases by automatically deciding the ideal engine settings. This ML-based element most shows OpenROAD's commitment to intelligent, non-human design methods.

• COPILOT: A cloud-enabled orchestrating system. COPILOT (Cloud Optimized Physical Implementation Leveraging OpenROAD Technology) manages distributed computing resources to accelerate OpenROAD. Anticipating failed jobs and focusing computation on complex subproblems (like DRC hotspots) helps to raise throughput. COPILOT has demonstrated significant speedups in practice (e.g., ~10× in detailed routing under specific conditions) by distributing work across multiple servers. Though in research and early release, COPILOT shows OpenROAD's move toward cloud and AI-assisted design.

• RTL-MP (RTL Macro-Placer): RTL-MP, sometimes known as RTL Macro-Placer, organizes logic based on RTL hierarchy and sets clusters as macroblocks, as was already said. Virtual connections and cluster weights are defined with dataflow and temporal affinity. Published the whole approach (ISPD 2022) by Kahng et al. Its primary characteristics, though, are that it adds "virtual connections" to record temporal routes, auto-clusters, and macros and solves the Clustering as a graph-partitioning issue. Consequently, the OpenROAD method may generate almost designer-quality layouts and start physical design significantly earlier, often even from RTL.

• TritonPart (Partitioner): It is a new partitioning tool under development to break sub-designs into partitions for enhanced hierarchy management. TritonPart seeks to enhance placement quality and scalability by breaking down large designs into smaller, more manageable pieces.

The Openroad initiative now includes all of these instruments and has tested them on real silicon devices. OpenROAD can incorporate GPU-accelerated and ML-guided versions suggested by academics as they evolve (DG-RePlAce, for example, uses GPUs for placement).

Main Algorithms

Many fresh ideas underpin OpenROAD's success:

• Analytic Placement (RePlAce): applies an electrostatics model of placement in conjunction with Nesterov's accelerated gradient methods. The density function is transformed to locally smooth overlaps (constraint-oriented smoothing) to enhance convergence and routability. Step sizes are automatically changed to maximize and direct effort where it is most required. This generates solutions with a notably lower wire length (HPWL) than past academic placers on benchmarks.

• Congestion Estimation: OpenROAD continuously computes routing congestion during placement by running Fast Route "on the fly." This congestion-driven orienting drives cells away from high-density locations. FastRoute essentially distributes nets to routing tracks in the placement area and calculates a fee for overflowing bins. The placement engine then combines a congestion cost term, akin to a pseudo-net, to guide items to less busy places.

• Clock Tree Balancing: TritonCTS generates balanced trees, often H-trees, hence minimizing skew. It adds one buffer at a time, top-down, and divides sinks in a top-down manner. Thanks to on-the-fly buffer characterization, it chooses the smallest buffers that meet several objectives. The technique can add extra buffers or shielding to the route to balance path lengths. While the exact methods are exclusive to TritonCTS, the application solves a buffered Steiner tree problem with balancing constraints.

• Detailed Routing (Maze + Rip-up/Repair): A*/ Lee's approach in TritonRoute's core search most likely finds each net's shortest legal paths, which then routinely resolves conflicts. In every iteration, nets are rerouted either individually or in groups; a cost map of the routing grid is updated (greater cost in busy or illegal areas). This repeated repair approach is often used in detailed routers. Multithreading enables TritonRoute to manage large chip sizes and maintain close database connectivity, differentiating itself from other solutions.

• Machine Learning Optimization: AutoTuner utilizes a large computing cluster and hyperparameter search techniques (random search or Bayesian optimization), the algorithm forecasts which factors increase PPA after multiple flow runs with different settings using machine learning. Based on hundreds of experiments, it emphasizes virtually perfect matches. This application of ML essentially replaces the need for human trial and error.

• High-Level Clustering (RTL-MP): RTL-MP, high-level Clustering, clusters logic using a dataflow affinity metric. It defines "virtual connections" between register clusters using logical hops and signal bit-width. Then, using user-defined constraints (maximum and minimum cluster sizes), a graph clustering problem is resolved. This logically aware clustering method's capacity to detect important temporal structures, even before physical placement, leads to better macro groups.

System Integration and CI Pipeline

The developers of OpenROAD place great emphasis on automation and quality control. The entire effort utilizes a thorough Continuous Integration system hosted in the cloud. On the Google Cloud Platform, a Jenkins-based pipeline automatically gathers the codebase, runs unit tests for every tool, and runs regression flows on actual benchmark designs. The test suite is continually expanding with new designs, especially successful OpenMPW/MPW chips, to ensure comprehensive coverage. For instance, OpenROAD's CI covers logic cores to fully SoCs, using over 80 tape-out designs from SkyWater shuttles as test cases. On a dashboard, metrics for each run, such as wire length, timing, and resource use, are gathered and monitored, allowing developers to identify early regressions.

The CI also runs Coverity static analysis scans of the code to identify common bugs and performs dynamic analysis for memory faults. OpenROAD targets NHIL 24-hour flows. Thus, the CI cluster is big enough to complete multi-threaded place-and-route jobs in a sensible time. Google generously supplies technical support for this infrastructure and cloud credits. The outcome is a constantly tested codebase that supports both production and research.

Support for Multiple Technology Nodes

OpenROAD's modular design enables it to manage practically any CMOS technology (LEF, Liberty), provided the user supplies the PDK data. Examples of exhibited nodes include the SkyWater 130 nm PDK, often used in educational MPWs; GlobalFoundries' 65 nm (CLN65LP) for CPU designs; GF 12 nm (12LP FinFET) for more complex SoCs; and Ascenium's anticipatory 7 nm ASAP7 PDK. For every node in the database, there is layer capacity, design rules, and time constraints. The FASTRoute global router of OpenROAD utilizes the layer track capacities from DEF and incorporates them into congestion calculations, thereby naturally handling higher-density 7 nm layers. Finally, OpenROAD has been assessed for processes spanning single-digit nanometer (nm) projections to more than 100 nm. Once the suitable PDK is loaded, its output GDSII can aim at any foundry.

Applications and Use Cases

Several genuine initiatives and demonstrative ideas have leveraged the OpenROAD flow. Among the engaging use scenarios are:

• SkyWater MPW and RISC-V SoCs: Based on OpenROAD, Google/Efabless shuttle efforts using SkyWater's 130 nm technology make use of an open-source OpenLane flow. Thousands of RISC-V projects, including RI5CY, MEMS controllers, and AES cores, have been taped out using this approach. With ORFS and OpenROAD, for example, a 16 nm SoC was built with an AES-128 crypto core, an Ibex RISC-V CPU, and sensor interfaces. The CI pipeline combines authentic OpenMPW designs from several shuttles to guarantee continuous compatibility.

• University SoCs (BlackParrot, others): OpenROAD has been applied in advanced nodes of academic RISC-V initiatives. The BlackParrot 12 nm open-source processor utilized OpenROAD's RTL-MP for its floorplan, resulting in a high-frequency, small-sized architecture. The OpenFASoC (Open Analog Mixed-Signal) project at the University of Michigan utilizes OpenROAD to automate the integration of analog IP. These initiatives demonstrate how easily one can control even large academic chips or gates valued in the millions of dollars.

• Army and Government Labs: OpenROAD piques defense research interests. Army Research Labs announced a research aiming at Intel 22FFL (22 nm FinFET) using OpenROAD. Part of German government-funded projects, such as the HEP-Alliance trust-hardware effort, OpenROAD, and ORFS (the VE-HEP SoC on SkyWater), have been utilized to tape out RISC-V and encrypted AES chips. Furthermore, the DARPA IDEA community organized an open 7nm design competition using the ASAP7 PDK, where teams utilized the OpenROAD flow to create RISC-V cores.

• Startups (Ascenium): OpenROAD is being utilized by semiconductor startups to reduce development expenses. Investigating microarchitectural variations using Bazel and several iterations of OpenROAD-flow-scripts, a Finnish company called Ascenium announced the design of an energy-efficient general-purpose processor utilizing OpenROAD on a 7 nm ASAP7 PDK. They observed improvements in tool stability and increased demand for capabilities such as hierarchical timing and automated macro placement. Their experience demonstrates how OpenROAD can help make complex designs more reasonably priced.

• Mixed-Signal and Analog Integration: OpenROAD has been extended to incorporate mixed-signal flows under the DARPA FASoC project. High-level analog circuits, such as DACs and voltage references, described in this pipeline are converted to Verilog before physical implementation. OpenFASoC then places and routes these analog blocks, just like digital macros would, using OpenROAD. For Google's SkyWater MPW-II, OpenROAD presented an all-digital LDO voltage regulator (D-LDO), for example, by grouping multiple LDO cells symmetrically and producing consistent power-stripe patterns. The automated flow (shown below) illustrates how OpenROAD's adaptability can be leveraged to integrate analog "schematic entry" (with a JSON specification) into a predominantly digital place and route (P&R) workflow. This demonstrates how OpenROAD, by viewing analog blocks as cell instances (utilizing OpenFASoC), can handle analog layouts.

• Contests and Education: OpenROAD finds great use in higher education. Other teaching tools, including the TinyTapeout, aid in educating chip designers. The 2022 OpenROAD-7nm Development Contest enabled students worldwide to design RISC-V cores using OpenROAD on ASAP7, thereby fostering an understanding of the design flow. More than 600 students and hobbyists have used OpenROAD in silicon chip design seminars.

New users of OpenROAD are still adopting it, as it has consistently demonstrated its ability to create tapeout-ready layouts for industry-standard procedures across various projects. Among its achievements are various working devices (AES+CPU, LDO arrays, etc.), as well as design contest wins. These initiatives also help OpenROAD (e.g., Ascenium's comments on handling large arrays and time hierarchies) by spotting flaws or new needs.

Technical Difficulties and Future Orientations

OpenROAD has made remarkable progress, but some challenges still exist, and areas requiring further study still warrant attention.

• Algorithmic scalability: sophisticated node design may make use of tens of millions of cells. Maintaining NHIL performance for large-scale designs calls for new algorithms. OpenROAD is examining multi-level and partitioning methods (such as TritonPart) to break down huge designs into smaller clusters. Extreme parallel routing and GPU acceleration, DG-RePlAce, for example, are also under research on quite parallel paths. Effective computer resource management, utilizing COPILOT and cloud scaling, will be crucial for the next generation of designs.

• Mixed-signal and Analog Support: Including analog circuitry in a fully automated process is naturally challenging. OpenFASoC has shown a possible path by considering analog blocks as cell macros, even if actual analog P&R (managing analog-specific optimizations) is still in its infancy. Stronger LVS/analog DRC checks, analog-aware parasitic estimates, and support for many voltage domains, as discussed by OpenFASoC, will be part of future enhancements. Building analog circuit libraries (such as OpenFASoC) can help OpenROAD's reach to be even more enlarged.

• AI/ML-Assisted Design: OpenROAD adjusts its settings (AutoTuner) using machine learning (ML), thereby supporting the design process. Reinforcement learning for routing learned placements, using neural networks to predict ideal layouts, and LLM-powered design assistants, such as EDA Copilot, that help users choose constraints, may all be part of future breakthroughs in artificial intelligence. Recent research, such as Intel's GenAI paper, highlights the growing trend of incorporating artificial intelligence models into chip design. Such technologies could be integrated into OpenROAD to provide suggestions for automated design decisions or recommendations for optimizations beyond numerical alterations.

• Commercial Adoption and Ecosystem Integration: OpenROAD's features, such as multi-corner timing and extensive process rule support, must align with business flows to be widely adopted. Attempts are made to integrate IP generation (standard cells, memory) and port it to more PDKs (from foundries like GlobalFoundries or IHP, for example). Under projects like the EU's FOSSi roadmap, OpenROAD is recognized as the foundation for open hardware innovation. Further success depends on working with PDK developers, expanding foundry support, and maybe certifying OpenROAD flows for industrial use.

OpenROAD has ultimately accomplished numerous early goals, including the creation of an independent, open-source backend capable of tape-out chips. Its future rests on increasing its commercial adoption, spanning mixed-signal, machine learning/artificial intelligence, and algorithm scalability. Research-wise, the project's roadmap includes utilizing artificial intelligence and the cloud to accelerate design further, as well as enhancing clock-tree algorithms, hierarchical timing analysis, and automated macro placement, as requested by users, including Ascenium. Driven by the community, OpenROAD is an open platform that aligns well with the development of these trends and continually pushes the boundaries of automated circuit design.