What if the next leap in AI didn’t rely on electrons at all? China just entered the global photonic chip race with a new pilot line that promises faster, more energy-efficient computing. This isn’t just about catching up—China’s approach could help unlock the next era of artificial intelligence, 6G, and quantum computing.
What’s the News?
On June 5, the Chip Hub for Integrated Photonics Xplore (CHIPX) at Shanghai Jiao Tong University announced that it has launched production of 6-inch thin-film lithium niobate (TFLN) photonic chips. These chips use light, not electricity, to transmit and process information—a major departure from traditional semiconductors.
While the US and Europe have already moved into photonic manufacturing, this is China’s first pilot production line for photonic chips. Yet it’s showing strong early performance. According to CHIPX, the line has achieved breakthrough metrics: over 110GHz modulation bandwidth, waveguide loss under 0.2 decibels/cm, and insertion loss below 3.5 decibels. These metrics are critical for signal integrity in high-speed optical communications.
TFLN is known for its high-speed, low-power performance, but its fragile nature made manufacturing difficult. Professor Jin Xianmin, director of CHIPX, said the stable line took 15 years of research and three years of building. The facility now runs over 110 advanced tools, using CMOS-compatible techniques and international-grade equipment.
Construction began in 2022 and focused on creating a stable, scalable system. The team implemented annealing and surface repair techniques to minimize optical losses, a key step in photonic chip efficiency. Unlike traditional chips, which focus on miniaturization, photonic chips demand ultra-smooth surfaces.
The production line currently outputs up to 12,000 wafers annually, and it has significantly reduced development times. What used to take a year can now be iterated weekly, accelerating quantum and AI hardware testing cycles.
The photonic chips aren’t just powerful. They are also versatile. According to CHIPX, the technology enables seamless integration between optical and electronic systems, ideal for cloud computing, data centers, AI inference engines, and next-gen telecom infrastructure.
Why It Matters
Photonic chips could revolutionize how AI models are trained and deployed. With faster data transfer and lower latency, they offer a scalable route to tackle today’s growing computing demands. That’s especially critical in edge computing, cloud infrastructure, and AI workloads that involve massive datasets.
If successful, China’s move into photonic manufacturing gives it strategic leverage in high-performance computing, a domain currently dominated by the US and a handful of allies. More broadly, the ability to rapidly iterate on chips can boost innovation in quantum technologies, biosensing, and LIDAR systems.
This is also a milestone in supply chain independence. By using both international tools and developing domestic alternatives, China is paving the way for long-term resilience in chip manufacturing—a vital issue in the current geopolitics of semiconductors.
💡Expert Insight
“These lithium niobate photonic chips can function not only as computing servers themselves but also as critical components for photonic-electronic integration,” said Professor Jin Xianmin. “They enable the high-speed connection of various computing resources.”
He added that the team envisions a future where optical transmission and computing are tightly merged to deliver energy-efficient, low-latency AI systems.
GazeOn’s Take
Expect China to keep pushing hard in photonic R&D. If CHIPX achieves stable scaling to 8-inch wafers, this could reshape global supply chains for high-performance chips. We may also see more public-private partnerships and increased investment across Asia’s AI hardware ecosystem.
💬 Reader Question
Could photonic chips finally crack the speed-efficiency ceiling holding back next-gen AI? Share your thoughts below.
About Author:
Eli Grid is a technology journalist covering the intersection of artificial intelligence, policy, and innovation. With a background in computational linguistics and over a decade of experience reporting on AI research and global tech strategy, Eli is known for his investigative features and clear, data-informed analysis. His reporting bridges the gap between technical breakthroughs and their real-world implications bringing readers timely, insightful stories from the front lines of the AI revolution. Eli’s work has been featured in leading tech outlets and cited by academic and policy institutions worldwide.
