[New Publication] The AI Infrastructure Revolution: Navigating the Shift to Optical-Electronic Integration - Published by CMC Research Co., Ltd.

The next AI hegemony will be determined not by GPUs, but by 'communication.' This report forecasts the CPO adoption curve, optical interconnects, supply chain restructuring, and opportunities for Japanese material manufacturers through 2035. It provides a thorough analysis of 224G-PAM4 limitations, CPO thermal design, liquid cooling, and yield issues, redefining AI infrastructure. This is an essential report for engineers seeking to understand the core of 'optical-electronic integration.' The rapid evolution of generative AI is fundamentally changing the structure of the semiconductor and data center industries. Previously, the center of AI competition was the improvement of GPU compute performance. However, the axis of competition is clearly shifting. Due to the massive scale of AI models and the expansion of distributed learning, the bottleneck has moved from 'compute performance' to 'data movement efficiency.' In next-generation AI clusters, thousands to tens of thousands of GPUs are connected simultaneously, exchanging vast amounts of data in real-time. If communication latency or bandwidth shortages occur, expensive GPU clusters remain idle, rapidly reducing overall system efficiency. In other words, the essence of AI infrastructure competition is changing from 'how fast a GPU you have' to 'how efficiently you can operate a massive AI cluster.' Amidst this structural change, traditional electrical wiring-based interconnects are approaching their limits. In the 224G-PAM4 generation, insertion loss, crosstalk, jitter, and heat generation during long-distance transmission of high-speed signals increase sharply, making compensation circuits like Retimers and DSPs essential. Consequently, power consumption in the communication section increases, putting pressure on the power efficiency and cooling costs of the entire AI data center. A promising solution to this challenge is 'opticalization.' Optical interconnects enable low-loss, high-speed transmission over long distances and offer superior power efficiency. In particular, Silicon Photonics (SiPh) and Co-Packaged Optics (CPO) are rapidly gaining prominence as core technologies supporting next-generation AI infrastructure. CPO, in particular, is expected to be a technology that achieves both high bandwidth and low power consumption by integrating optical engines near switch ASICs or AI accelerators, thereby minimizing electrical wiring length. However, its implementation involves extremely advanced challenges different from traditional semiconductor packaging, such as thermal design, liquid cooling, warpage control, optical coupling precision, and mass production yield. In short, the evolution of AI infrastructure is shifting from a mere GPU performance race to a 'packaging technology race.' More importantly, this change is beginning to restructure the entire supply chain. Optical module manufacturers are changing their roles to become Optical Engine suppliers, and OSAT companies are required to have photonics packaging capabilities. In addition, the importance of material technologies where Japanese companies have strengths, such as low-dielectric materials, glass substrates, high-thermal-conductivity materials, and precision adhesive materials, is rising rapidly. Competitive advantage in AI infrastructure no longer depends on semiconductors alone, but on whether one can integrate 'optics,' 'packaging,' 'materials,' and 'cooling.' This report comprehensively analyzes this major shift in AI infrastructure from the perspective of 'optical-electronic integration.'