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Intel’s EMIB Packaging Is Growing Rapidly — Silicon Capacitors Are Taking Off Too Silicon capacitors are poised for explosive growth in the AI semiconductor space. Intel has been found to be planning a large-scale adoption of silicon capacitors starting next year, in order to enhance the performance of its in-house 2.5D packaging technology, “EMIB.” The most clearly visible source of demand is Google. Google plans to launch its next-generation AI accelerator, “v8e,” in the second half of next year, and has adopted an EMIB substrate with embedded silicon capacitors for that chip. With other Big Tech companies such as Amazon also currently applying EMIB, analysts say demand could increase sharply. According to industry sources on the 27th, Intel plans to apply silicon capacitors to its 2.5D packaging starting next year. Intel Adopts “Silicon Capacitors” for 2.5D Packaging… Google AI Chip Gets First Application 2.5D is an advanced packaging technology that inserts a thin-film interposer between the semiconductor and the substrate. Because it can connect circuits at higher density compared with conventional packaging that uses only a substrate, demand is rising in the AI and HPC fields. To improve cost efficiency in 2.5D packaging, Intel devised its own technology called EMIB. Rather than using a broad, spread-out interposer, EMIB connects chip to chip using a small silicon bridge. Since bridges only need to be placed where chip-to-chip connections are required, chips can be arranged more flexibly and efficiently. Recently, EMIB has been drawing attention as an alternative to TSMC, which had been leading the existing 2.5D packaging market. This is because TSMC’s 2.5D packaging capacity is suffering from a supply shortage amid the rapid development of the AI industry. Indeed, global Big Tech player Google is also paying attention to EMIB. Google has decided to adopt EMIB for its in-house AI semiconductor “v8e,” which it plans to launch in the second half of next year. Under this structure, TSMC handles chip mass production, MediaTek handles design and manufacturing support, and Intel handles packaging. However, there have been concerns that EMIB is gradually showing limitations in providing stable power supply for AI semiconductors, which consume large amounts of power. Accordingly, Intel plans to introduce new technologies such as silicon capacitors and through-silicon vias (TSV) to ensure stable packaging for the v8e. A capacitor is a component that stores and releases electricity in an electronic circuit. In the case of silicon capacitors, their resistance (ESL/ESR) is more than 100 times lower than that of conventional multilayer ceramic capacitors (MLCC), minimizing the signal loss that occurs in high-performance semiconductors. They can also be designed in an ultra-thin structure based on a silicon wafer, enabling high-density integration. A semiconductor industry official explained, “Because the voltage drop (the phenomenon of voltage decreasing) that occurs in the high-frequency region within AI chips is difficult to solve with MLCC, we understand that Intel is adopting silicon capacitors as a solution,” adding, “The relevant supply chain is now in place, and mass production is set to begin in earnest next year.” EMIB-T Is Already on a Growth Trajectory — The Related Ecosystem and Market Are Expanding Together Intel has also inserted TSVs, which serve as power-delivery channels, into the silicon bridge. The key point is that by using TSVs to shorten the power-delivery path between the substrate and the chip, Intel has improved power efficiency and signal integrity. Intel calls this “EMIB-T.” The industry expects the EMIB-T and silicon capacitor markets to grow rapidly. This is because Japan’s Ibiden — one of the major companies that mass-produces semiconductor substrates for EMIB-T — is aggressively pursuing capital investment. Previously, Ibiden had planned to build its Kawashima (Gama) plant in Gifu Prefecture as a substrate plant for Intel CPUs. However, it postponed that schedule and decided in the first half of this year to officially convert the Gama plant into a mass-production line for EMIB-T substrates. The investment is 220 billion yen (about KRW 2.1 trillion). In its recent earnings announcement, Ibiden stated, “Operation of the Gama plant will begin in 2027 and enter full-scale mass production in 2028,” adding, “EMIB-T substrate capacity is currently far short of demand. However, adding further capacity is quite difficult, so we are discussing options with our customers.” A semiconductor industry official explained, “Ibiden’s EMIB-T-dedicated line is being built with most of the investment coming from customers such as Google, Amazon, and Intel,” adding, “This demonstrates that AI semiconductors based on EMIB-T will grow significantly going forward, and silicon capacitors are likely to expand alongside them.”

As companies shift their attention towards AI as a means of transforming their businesses, they are looking to McKinsey and its fellow consultancies for help. But they are also turning to less conventional partners

Elon Musk was asked how fast AI is moving. His answer wasn’t about the technology. It was about the one man who got it all right and was still too conservative. Musk: “I have to give credit to Ray Kurzweil in being actually remarkably accurate in his predictions. If anything, I think he was perhaps a bit conservative in his predictions.” Kurzweil spent 30 years making forecasts that made serious people uncomfortable. He predicted timelines that sounded impossible. He was mocked for it. He was right about nearly all of them. And Musk just called him conservative. Musk: “The dedicated AI compute appears to be growing by a factor of 10 every six months.” 10x every six months. Musk: “Almost a 100x improvement per year, at least for the next few years.” Moore’s Law was a 2x improvement every two years. That single curve drove every technological shift of the last 50 years. The internet. Smartphones. Cloud computing. All of it rode a 2x curve. AI is on a 100x curve. And the current infrastructure isn’t running beside the new one. It’s becoming it. Musk: “Probably a lot of the data centers, maybe most of the data centers that currently do conventional compute, will transition to AI compute.” Everything that runs the world you know is being rewired for the world that comes next. Human beings process the future in straight lines. We take the speed of the last decade and project it forward. Exponential growth doesn’t work that way. It’s invisible until it’s everywhere. The most aggressive forecaster in the history of technology was too conservative. That’s not about Kurzweil being wrong about the direction. That’s about the human brain being wrong about the speed. The limit was never the technology. It was the organ we use to comprehend it. And that organ hasn’t been upgraded in 200,000 years.

As companies shift their attention towards AI as a means of transforming their businesses, they are looking to McKinsey and its fellow consultancies for help. But they are also turning to less conventional partners

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Today we release Token Superposition Training (TST), a modification to the standard LLM pretraining loop that produces a 2-3× wall-clock speedup at matched FLOPs without changing the model architecture, optimizer, tokenizer, or training data. During the first third of training, the model reads and predicts contiguous bags of tokens, averaging their embeddings on the input side and predicting the next bag with a modified cross-entropy on the output side. For the remainder of the run, it trains normally on next-token prediction. The inference-time model is identical to one produced by conventional pretraining. Validated at 270M, 600M, and 3B dense scales, and at 10B-A1B MoE. The work on TST was led by @bloc97_, @gigant_theo, and @theemozilla.

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