Core Insights - The global optical design software market is projected to reach $400 million by 2031, with a compound annual growth rate (CAGR) of 8.6% in the coming years [5]. - The market is characterized by a strong presence of leading manufacturers, with the top five companies holding approximately 81.0% of the market share in 2024 [9]. - The primary product type is locally deployed software, accounting for about 85.9% of the market, while optical instruments represent the main application area, capturing around 65.1% of the demand [12][14]. Market Trends - The optical design software industry is increasingly integrating with the optoelectronic sector, driven by the rapid growth in demand for high-performance optical systems in emerging fields such as 5G communication, autonomous driving, AR/VR, and biomedical imaging [17]. - Multi-physics simulation and system-level design are becoming mainstream, as future optical systems require integration of optical, thermal, structural, and electrical simulations [18]. - The adoption of intelligent and automated design optimization is accelerating, with AI technologies enabling features like automatic layout and parameter optimization, significantly reducing product development cycles [19]. - Cloud collaboration and ecosystem development are evolving, with a shift from local installations to cloud-based solutions that support multi-user collaboration and on-demand payment models [21]. Key Drivers - The demand for optical design software is expanding from traditional optical systems to comprehensive optoelectronic system designs, influenced by advancements in autonomous driving, AR/VR, and other high-tech applications [22]. - Multi-physics coupling and cross-platform collaboration are becoming standard, as applications in high-power lasers and aerospace require integrated design processes that assess the impact of various physical factors on imaging quality [23]. - The industry is increasingly leveraging intelligent optimization and automation to enhance efficiency in complex design scenarios, allowing engineers to focus on defining requirements and evaluating solutions [24]. - The transition to cloud-based deployment and the establishment of integrated service ecosystems are being driven by the need for localized support and collaboration across different regions [25]. Market Challenges - The optical design software market is characterized by high entry barriers and strong user loyalty, making it difficult for new entrants to compete with established players [26]. - There is a scarcity of skilled talent capable of utilizing optical design software effectively, which limits software utilization rates and affects renewal intentions [27]. - Localized demands coexist with competition from international giants, creating challenges for domestic software providers in meeting both global standards and local expectations [28]. - Issues related to software piracy and the need for ecosystem development are pressuring profit margins, as customers increasingly seek comprehensive solutions that combine software with databases and support services [29].

Core Viewpoint - The EDA industry is undergoing a methodological reconstruction due to the exponential increase in AI model scale and computing power requirements, transitioning from a "design tool" to the underlying operating system of AI computing systems [1][3]. Group 1: Challenges in EDA - The main challenge for domestic EDA in ensuring the autonomy of AI computing systems lies in the need to consider the entire system rather than just the chip, especially as Moore's Law becomes less applicable [3]. - The evolution of computing architecture from single chips to multi-chiplets and supernodes presents new challenges in maintaining flexibility, scalability, and high-bandwidth interconnects [4]. Group 2: Chiplet Architecture - The Chiplet design requires new verification and collaboration processes, with a two-phase approach: the first phase focuses on integrating computing and storage through a 2.5D structure, while the second phase involves hybrid bonding for 3D stacking [4]. - The integration of various components such as sensors, storage, and RF devices into a compact design necessitates multi-physical field collaborative analysis, which increases the complexity of simulation across different scales [4][6]. Group 3: System-Level Simulation - System-level simulation differs significantly from traditional EDA, as it must account for multi-physical field interactions and the potential for issues arising from high current and impedance fluctuations [5]. - Chiplet architecture offers advantages in system-level considerations, allowing for a more comprehensive approach to design and integration [5]. Group 4: AI Integration in EDA - The strategy of "EDA for AI" focuses on providing comprehensive solutions from chip design to system integration, addressing the challenges posed by AI's increasing computational demands [10]. - The "AI + EDA" strategy aims to integrate AI models into the design and simulation processes, significantly enhancing efficiency and enabling a shift from rule-driven to data-driven design approaches [12]. Group 5: Future Outlook - The future of EDA in the AI era is characterized by cross-scale, cross-physical, and cross-system engineering, with expectations for more domestic design tools to become practical and for system-level issues to be resolved during the simulation phase [14]. - The company is positioned as a key player in this transformation, leveraging advancements in Chiplet technology, supernodes, and AI factories to enhance the stability and power of AI computing infrastructure [14].