The Tao (τ) Law Makes EDA Go Viral

marsbitPublished on 2026-06-12Last updated on 2026-06-12

Abstract

In May 2026, Huawei's semiconductor division introduced the "Tao (τ) Law" at IEEE ISCAS, shifting the industry focus from Moore's Law's geometric scaling to "time scaling." Unlike traditional approaches relying on transistor miniaturization, τ Law optimizes the time constant (τ) across device, circuit, chip, and system levels to improve information processing speed and efficiency. Huawei has already applied this principle, mass-producing 381 chips across various applications, with a target to achieve performance equivalent to 1.4nm technology by 2031. The implementation of τ Law, involving techniques like Chiplet, 3DIC, and Logic Folding, places new demands on EDA tools, highlighting gaps in current offerings. Traditional 2D or pseudo-3D EDA flows lack native support for true 3D design, cross-layer co-optimization (STCO), and coupled multi-physics analysis (thermal, power, stress), which are crucial for advanced integration. Chinese EDA companies, such as Empyrean Software, Primarius Technologies, and Xpeedic, are evolving from point-tool specialists to providing full-flow, system-level solutions. For instance, Peking University has developed a prototype "true 3D" EDA tool showing significant improvements in wirelength and timing. Empyrean Software has also launched a comprehensive 3DIC design and verification platform. The τ Law framework presents an opportunity for the domestic EDA industry to transition from achieving basic functionality to developing robust, integrated ...

On May 25, 2026, at IEEE ISCAS 2026, He Tingbo, President of Huawei's Semiconductor Business Unit, introduced a key concept: The Tao (τ) Law. τ, the time constant in circuit theory, determines the speed at which a signal switches from one state to another. This marks the first time a Chinese enterprise has proposed a new principle guiding industrial development in the global semiconductor field.

More concretely, over the past six years, Huawei has mass-produced 381 chip models based on this law, covering core scenarios such as wireless base stations, AI inference, and network processors. This is not a blueprint; it's a proven path. It is projected that by 2031, high-end chips based on the τ law will achieve performance equivalent to a 1.4nm process node, maintaining long-term competitive parity with international mainstream technology roadmaps.

Today, this Greek letter is quietly reshaping the value structure of the semiconductor industry and bringing EDA from the backstage to the forefront.

To understand what impact τ will have on the EDA industry, one must first grasp what the τ law actually entails.

"Time Miniaturization" Has Just Taken the Stage: What's the Basis for the τ Law?

Moore's Law, proposed by Intel co-founder Gordon Moore in 1965, states that the number of transistors on an integrated circuit doubles approximately every 18 to 24 months, while performance increases and cost decreases.

For over half a century, this logic has functioned effectively, underpinning PCs, the internet, smartphones, and today's artificial intelligence. The industry chain also formed a tacit rhythm around it—lithography machines, materials, design, all sectors advanced collaboratively on the path of miniaturization. However, around 2000, dozens of foundries could follow the most advanced process nodes. By 2025, that number had plummeted to just three: TSMC, Samsung, and Intel, with the price for a single TSMC 2nm wafer even exceeding $30,000.

It can be said that the dividends of Moore's Law are gradually diminishing. The industry has now explored multiple technical paths, including "Huang's Law" proposed by NVIDIA CEO Jensen Huang, "More than Moore" proposed by the International Technology Roadmap for Semiconductors (ITRS), and the Chiplet and advanced packaging technologies primarily promoted by AMD and TSMC. Among these, Huang's Law emphasizes doubling the AI inference performance of a single GPU chip annually but still relies on process iteration and core stacking, essentially continuing the geometric scaling approach. "More than Moore" adds value through the integration of analog/RF/sensor functionalities but cannot directly solve the digital logic delay wall problem. Chiplet, while using "building blocks" to alleviate yield and cost issues, introduces significant inter-die interconnect delays, which may become a bottleneck in scenarios highly sensitive to latency.

Most of these solutions still adhere to the logic of "geometric miniaturization" or functional stacking, which differs fundamentally from the τ law.

The core of the τ law is "time miniaturization" replacing "geometric miniaturization," constituting a complete optimization system spanning four levels: device, circuit, chip, and system. It is suitable for large-scale system-level performance enhancement and holds particular advantages in AI and heterogeneous computing scenarios.

He Tingbo elaborated on this in detail: At the device level, by optimizing transistor and interconnect resistance along with parasitic capacitance, the device-level time constant τ is minimized from the physical foundation to the greatest extent. At the circuit level, logical folding technology breaks through the physical boundaries of traditional planar layouts, significantly shortening the wire length of critical paths and effectively reducing signal propagation resistance and capacitive loads, achieving substantial improvements in transistor density and circuit performance. At the chip level, through full-stack software-architecture-chip co-design, fine-grained control of instruction and data flow based on actual workloads enhances system-level parallelism and efficiency, significantly reducing end-to-end execution time. At the system level, defining the Lingqu Bus reconstructs computing system interconnect protocols, enabling unified memory addressing and native memory semantics across super-nodes, greatly reducing system communication latency.

In comparison, the τ law aligns more closely with the core essence of chip computing power: the primary function of a chip is information processing, and end-users are also more concerned with the latency performance of information processing rather than transistor count or process dimensions. This law provides a completely new technical roadmap for chip design that departs from reliance on pure process scaling, meaning it holds the potential to create chips with satisfactory comprehensive performance even without using the most advanced lithography equipment. Therefore, it is not contradictory to Moore's Law; the two are compatible. It can be understood as: Moore's Law continuously draws finer grids on a flat plane, while the τ Law folds the paper, using three-dimensional space to obtain shorter signal paths.

Notably, the implementation of each layer of the τ law relies on a key player—EDA. It is no longer a "drawing tool" in the traditional sense but has become the central nervous system translating "time miniaturization" from theory into physical chips.

A Huawei paper indicates that on the technical route, a co-existing and superimposed approach of Chiplet advanced packaging, three-dimensional integrated circuits (3DIC), and Logic Folding is adopted to achieve recombination and optimization at different granularities in vertical integration. To achieve hardware integration density improvement exceeding 100-fold by 2035, three major challenges are faced: an EDA toolchain generation gap, cross-wafer process variation, and the law of conservation of energy.

Andrew B. Kahng, Distinguished Professor of Computer Science and Engineering and Electrical and Computer Engineering at the University of California, San Diego, also noted that as the "tailwind" from the traditional "Moore's Law" gradually weakens, these fundamental objectives in EDA and physical design will become increasingly important.

Therefore, EDA has been repositioned to the center of the table.

What New Requirements Does the Tao (τ) Law Pose for EDA?

Regarding the new demands the τ law places on EDA tools and the existing shortcomings of traditional EDA tools, the author engaged in discussions with industry practitioners.

First, the lack of native true 3D design and cross-layer co-optimization capabilities highlights the importance of STCO.

Firstly, Peking University pointed out that traditional 2D design flows, and even the current mainstream "pseudo-3D" flow—where after synthesis each module is "fixed" onto a specific die at once, and then 2D EDA tools are used to implement each die sequentially—cannot achieve cell-level flexible allocation across layers.

Native 3D EDA tools integrate multiple dies into a unified three-dimensional design space, supporting free placement of standard cells across dies while enabling cross-die logic restructuring and global optimization. This provides critical support for translating the logic folding concept from design theory into physical implementation.

"Pseudo-3D" flow vs. "True-3D" flow. Source: Peking University

Additionally, there is a deficiency in cross-layer co-optimization capabilities. Xpeedic Semiconductor told Semiconductor Industry Insights: Chiplet, 3DIC, and LogicFolding are implementations at different granularities along the same vertical integration main line.

Chiplet combines heterogeneous dies in a 2.5D or 3D manner at the packaging level, moving communication originally within a monolithic SoC to between dies via interconnect standards like UCIe, trading modularity for yield and flexibility. 3DIC further introduces high-density TSVs and hybrid bonding between dies, vertically stacking logic, memory, and analog functions within the same package, compressing interconnect distances from millimeter to micrometer scales. LogicFolding goes a step further—it does not establish interconnects between dies but rather vertically splits and re-distributes "the internal logic of a single chip itself" at the active layer level, allowing the hybrid bonding interface to participate directly in the timing optimization of critical paths like an additional metal layer.

The three are not substitutes but co-exist in a superimposed manner within advanced packaging systems. This superposition brings a fundamental design engineering challenge: when a single package involves UCIe interconnects between Chiplets, inter-layer hybrid bonding in 3D, and intra-die LogicFolding critical path folding, the analysis boundaries for signal integrity, power integrity, thermal distribution, and mechanical stress can no longer be closed independently at any single level.

The proposal of STCO (System-Technology Co-Optimization) aims precisely to break down this fragmentation from a methodological perspective. It demands treating logical architecture, physical layout, multi-physics fields, packaging structure, and even workloads as a unified design space, conducting joint optimization searches across disciplines and abstraction levels. And this capability is the most fundamental gap in the current EDA toolchain.

Second, the lack of multi-physics coupling.

This is one of the most hidden yet critical weaknesses of traditional EDA tools. In the monolithic chip era, power delivery analysis, thermal simulation, and stress calculation belonged to separate, independent toolchains, each with its own modeling, solving, and signoff. But in 3D stacking, this model is no longer fully viable. After vertical integration of multiple dies, power density multiplies, heat dissipation paths become highly asymmetric, and inter-layer temperature differences increase. The resulting thermal expansion mismatch propagates through microbump and hybrid bonding interfaces within the stacked structure, both skewing device electrical characteristics and posing mechanical reliability risks.

What Capabilities Do EDA Vendors Need to Build?

Currently, domestic EDA companies mostly focus on point breakthroughs, tackling tough challenges in their respective specialized segments. From analog simulation to physical verification, from yield enhancement to layout design, a group of excellent domestic EDA enterprises have already formed usable and competitive point tools across many areas.

For example, Empyrean Technology is one of the earliest companies in China engaged in EDA R&D. Empyrean Technology, with analog EDA as its foundation, has gradually expanded into digital, advanced packaging, and other fields, striving to build a full-flow toolchain. Primarius Technologies follows an "underlying penetration" route. It doesn't directly tackle the full flow but relentlessly focuses on device modeling and circuit simulation. UniVista is a leading domestic digital EDA company, representing the full-flow/platform type. Xpeedic Technology chose to show its strength in the most challenging "signoff" stage. Xpeedic Semiconductor primarily focuses on "advanced packaging." Semitronix emphasizes yield enhancement and is the only company capable of forming a complete closed loop through "equipment data collection + software data analysis."

The τ law is expected to propel domestic EDA from "point tool localization" to an industrial software foundation characterized by "full-flow, cross-level, strong co-optimization." This means the EDA toolchain will no longer merely undertake auxiliary functions like circuit drawing, layout design, and backend verification. Instead, it will need to be comprehensively embedded in all key processes across the entire chain: device modeling, PDK construction, circuit simulation, parasitic parameter extraction, timing/power analysis, physical verification, advanced packaging, and system-level co-optimization.

On May 26, the School of Integrated Circuits at Peking University announced a key breakthrough in the prototype of a "true 3D" EDA tool developed to meet the Logic Folding demands of the Tao Law. This tool supports comprehensive three-dimensional space co-optimization, enables cross-die logic free allocation and joint thermal optimization, and can cover designs with tens of millions of instances. Compared to traditional "pseudo-3D" methods, Peking University's "true 3D" EDA achieves: an average wire length reduction of approximately 30%; WNS improvement of about 6%, TNS improvement of about 12%; and a peak temperature reduction of over 3%. The tool has currently completed industrial-level design verification, with subsequent plans to extend to multi-die stacking and heterogeneous integration scenarios, aiming to fill the key gap in 3D chip design.

On the same day, an investor asked Empyrean Technology on an interactive platform: Against the backdrop of the post-Moore era, the industry believes the importance of EDA is evolving from a traditional design tool towards a "system-level performance optimization platform." What is the company's view on the strategic value of future EDA in logic folding, timing optimization, and multi-chip co-optimization?

Empyrean Technology subsequently responded: The company foresightedly observed that current AI, GPU, memory, and other chips are leveraging 3DIC technology to break through the bottlenecks of advanced processes and computing power in the post-Moore era. It has made early-stage layouts in the 3DIC design EDA field, constructing a full-flow solution covering from heterogeneous integrated 3D chip co-design to verification, filling the gap for high-end 3DIC design tools in China, and is the only domestic provider of a full-flow 3DIC design and verification EDA solution. The company launched the first industry-leading Argus 3DIC physical verification platform, fully supporting 2.5D/3D heterogeneous integrated packaging design, enabling full-link physical verification from 3DIC diversified co-design to packaging.

Thus, a path gradually becomes clear: from the theoretical guidance of the τ law, to system architecture definition, and then to the strengthening of the domestic EDA toolchain. In the coming years, vendors who first introduce industrially validated closed-loop solutions for timing closure in logic folding, 3D multi-physics coupling signoff, and full-stack STCO co-optimization are likely to occupy a more proactive position in the "time miniaturization" trend. For domestic EDA, this may provide a window period to shift from point-tool catch-up to building full-stack capabilities—no longer merely aiming for "usable," but continuously evolving towards full-stack "excellent usability."

This article is from the WeChat public account "Semiconductor Industry Insights" (ID: ICViews), author: Feng Ning

Related Questions

QWhat is the core concept of the Tao (τ) Law proposed by Huawei, and how does it differ fundamentally from Moore's Law?

AThe Tao (τ) Law proposes 'time miniaturization' as an alternative to Moore's Law's 'geometric scaling.' Its core is optimizing the time constant (τ) across device, circuit, chip, and system levels to reduce signal delay and improve performance. Unlike Moore's Law's focus on transistor count and chip size, τ Law focuses on optimizing the speed of information processing itself, offering a new technical route that is less dependent on cutting-edge lithography.

QAccording to the article, what new challenges and requirements does the Tao (τ) Law pose for EDA tools?

AThe Tao (τ) Law demands EDA tools to evolve from traditional 2D 'drawing tools' into a central nervous system for 'time miniaturization.' Key new requirements include: 1) Native true-3D design and cross-layer collaborative optimization (STCO) capabilities, moving beyond 'pseudo-3D' flows. 2) The ability to handle multi-physics coupling (e.g., thermal, stress, power integrity) in 3D stacked environments, as these effects can no longer be analyzed in isolation.

QHow do the technical approaches of Chiplet, 3DIC, and LogicFolding relate to each other in the context of implementing the Tao (τ) Law, as described in the article?

AChiplet, 3DIC, and LogicFolding are not mutually exclusive but represent different granularities of vertical integration that coexist and stack within the advanced packaging framework to implement τ Law principles. Chiplet enables modular die integration; 3DIC further reduces interconnect distances via vertical stacking; and LogicFolding takes it a step further by splitting and re-distributing a single chip's internal logic itself across active layers for critical path timing optimization. Their combination necessitates System Technology Co-Optimization (STCO).

QWhat are some examples of Chinese EDA companies mentioned in the article, and what are their respective focuses or strengths?

AThe article mentions several Chinese EDA companies and their specializations: Empyrean (Huada Jiutian): Focuses on full-flow tools, with strength in analog EDA and a claim to be China's only full-flow 3DIC design verification provider. Primarius (Gailun Electronics): Focuses on device modeling and circuit simulation from the bottom up. Unicore (Hejian Gongruan): A leader in digital EDA and full-platform solutions. Xepic (Xingxin Technology): Specializes in sign-off verification. Xpeedic (Xinhe Bandaoti): Focuses on advanced packaging solutions. GigaDevice (Guangliwe): Specializes in yield enhancement through a data-centric closed-loop approach.

QWhat breakthrough related to EDA tools for the Tao (τ) Law was announced by Peking University in May 2026, and what were its reported benefits?

APeking University announced a key breakthrough in a 'true-3D' EDA tool prototype for LogicFolding requirements under the Tao (τ) Law. Compared to traditional 'pseudo-3D' flows, this tool achieved: an average wire length reduction of ~30%, WNS improvement of ~6%, TNS improvement of ~12%, and a peak temperature reduction of over 3%. It supports full 3D spatial co-optimization, cross-die logic allocation, and joint thermal optimization for designs with tens of millions of instances.

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Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

723 Total ViewsPublished 2025.01.14Updated 2025.01.14

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