Lightmatter, a recognized leader in photonic supercomputing, has announced a strategic collaboration with Synopsys, a global authority in engineering solutions spanning silicon to full systems. Through this partnership, the two companies plan to integrate Synopsys’ silicon-proven 224G SerDes and UCIe IP, built for advanced 3nm process technology, into Lightmatter’s Passage 3D Co-Packaged Optics (CPO) platform. As a result, the collaboration aims to deliver a robust, low-latency optical interconnect solution capable of supporting the rapidly expanding demands of next-generation AI infrastructure.

As AI workloads continue to scale in both size and complexity, the limitations of traditional electrical interconnects are becoming increasingly evident. Therefore, Lightmatter is focusing on optimizing the electrical-to-optical interface within its Passage CPO platform. By combining Synopsys’ high-speed interface IP with Lightmatter’s 3D photonic engine, the companies are working to enable seamless, high-bandwidth connectivity between advanced AI accelerators, XPUs, and switching systems. This approach is expected to unlock new performance and efficiency gains across large-scale AI deployments.

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Highlighting the technical importance of the collaboration, Ritesh Jain, SVP of Engineering & Operations at Lightmatter, stated, “As AI models grow in size and complexity, the efficiency of the electrical interface to the optical engine becomes a critical design factor. Integrating Synopsys’ advanced and silicon-proven 224G SerDes and UCIe IP into our Passage platform allows us to provide a comprehensive, high-performance, high-volume manufacturing (HVM)-ready solution. This collaboration will help facilitate Passage CPO integration into our customers’ XPU and switch designs to achieve maximum performance, reliability, and power efficiency.”

From a technology perspective, the partnership delivers several tangible advantages. First, the optimized 224G SerDes and UCIe IP significantly enhance bandwidth while minimizing latency, aligning with the inherent energy efficiency benefits of Lightmatter’s 3D photonic architecture. Additionally, the use of pre-verified, silicon-proven IP reduces overall design risk, enabling customers to move forward with greater confidence and predictability. Moreover, by leveraging Synopsys’ AI-powered electronic design automation (EDA) and system-level tools such as 3DIC Compiler, the Lumerical product suite, and OptoCompiler Lightmatter can accelerate the co-design process for electrical and photonic components, ultimately shortening time-to-market.

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Neeraj Paliwal, senior vice president of IP product management at Synopsys, emphasized the broader industry impact, saying, “As AI and high-performance computing systems scale in complexity and performance, electrical interfaces to advanced photonic and accelerator engines become a critical design enabler. By integrating our silicon-proven 224G SerDes and UCIe IP into Lightmatter’s Passage 3D Co-Packaged Optics platform, we’re delivering a high-bandwidth, low-latency, and energy-efficient solution ready for scale up and out connectivity. This collaboration bridges silicon-centric systems and emerging 3D photonic architectures, while reinforcing Synopsys’ commitment to advancing the photonic IC design ecosystem with industry-leading tools and flows.”

Industry analysts also see the collaboration as a meaningful step forward for the CPO market. Alan Weckel, Founder and Technology Analyst at 650 Group, commented, “The integration of industry-leading high-speed SerDes IP optimized for advanced 3D photonic interconnects is a vital step in the maturation of the CPO ecosystem. This collaboration between Synopsys and Lightmatter addresses the critical path to market for CPO-enabled next-generation AI silicon, providing hyperscalers with a validated, high-performance roadmap to scale AI clusters beyond the limits of shoreline-bound interconnect alternatives.”

Overall, this partnership underscores how tightly integrated silicon and photonic technologies are becoming essential to the future of scalable, energy-efficient AI infrastructure.

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