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T-glass, the bottleneck: one Japanese firm controls 90% of a key AI chip material

Nittobo supplies roughly 90% of the world's T-glass, the specialist glass-fiber cloth inside every advanced AI chip package, and demand has outrun supply. Prices are up 20 to 30% and new capacity will not reach the market until mid-2027.

TechnologyExplainerRachel NwosuPublished: 28 September 20263 min readSources 1
T-glass, the bottleneck: one Japanese firm controls 90% of a key AI chip material

T-glass is not a chip. It is a low-CTE (coefficient of thermal expansion) glass cloth woven into the organic core of IC substrates, the interconnect layer between a chip and its printed circuit board. Tom's Hardware reported on 9 March that the material keeps large, high-heat packages dimensionally stable as AI processors grow bigger and run hotter.

One company makes almost all of it.

According to Tom's Hardware, Nittobo of Japan controls roughly 90% of global T-glass supply. No manufacturer can spin up a new production line quickly. The process requires specialised electric melting furnaces running between 1,600 and 1,700°C. Workers then spin the melted silica-rich glass into yarn and weave it into an ultrathin cloth. Scaling this takes years of investment and expertise.

The alternative, E-glass, is cheaper but mainly used in lower-end chips such as microcontrollers and older mobile processors. For massive 2.5D and 3D packaging, and therefore for AI accelerators, T-glass is the preferred option, per the same report.

What the shortage looks like in numbers

Nittobo is tripling capacity at its Fukushima plant, but the new supply will not arrive on the market until mid-2027. In the meantime, Tom's Hardware reports prices have risen between 20 and 30%. Lead times for downstream materials such as copper-clad laminates have stretched from a normal 8 to 10 weeks to beyond 20.

With T-glass supply even more constrained now, suppliers are no longer providing lead times, said Bill Ho, analyst at Yuanta.

Demand is the driver. Hyperscalers keep ordering larger chip packages that consume more T-glass per unit. Nvidia data cited by Tom's Hardware shows interposer sizes growing from 814mm² for the Hopper architecture to 1,700mm² for Blackwell, a 109% increase. The forthcoming Rubin and Feynman generations will scale further.

Bank of America estimates Nittobo's electronic materials segment will nearly double sales from ¥40.9 billion ($266 million) in 2025 to ¥87.7 billion by March 2028, with operating margins approaching 48%.

Why replacement is hard, and who is scrambling

Bilal Hachemi, an analyst at Yole Group who tracks the IC substrate supply chain, told Tom's Hardware Premium that T-glass has specific dielectric and CTE values that work better for AI chips, especially the organic core. He also pointed to thin industry margins: even modest demand surges can trigger shortages.

Hachemi said Nvidia reaching directly to an upstream material supplier like Nittobo was unprecedented, the first time he had seen it in that part of the supply chain. The worry is that once Nvidia locks down its share, rival chip buyers fight over the remainder.

Nittobo is not standing still. It is doubling raw yarn capacity at its Taiwan plant and importing yarn back to Japan for cloth manufacturing. It has also struck a collaboration deal with Nanya Plastics to outsource some weaving, according to Tom's Hardware. By 2027, roughly 20% of Nittobo's glass cloth is expected to be woven by Nanya.

That Nittobo is partnering with one of its biggest competitors to ease the bottleneck says as much about the market as any price figure.

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  1. 01Shortages of crucial chip packaging material threatens AI accelerator supply chainsEN

All figures and quotations in this text come from the sources listed below.

Content prepared by the editorial team with AI assistance.

Rachel Nwosu

Rachel Nwosu

AI, models and technology

Rachel Nwosu covers AI, models and technology for FLASH24, working from public model documentation, benchmark releases and repository histories rather than press summaries, and she skips announcements that arrive without reproducible numbers. She checks training-data claims against dataset cards and reruns reported metrics where code is available. She spends much of her week interviewing researchers and engineers, tracking model launch calendars, and comparing vendor benchmarks with independent evaluations. Outside the desk she runs 3D printers, restores old computers, and tests how models learn from internet junk. She does not publish benchmark figures she cannot trace to a source.

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