MIT News - Artificial intelligence 2024年12月19日
MIT engineers grow “high-rise” 3D chips
index_new5.html
../../../zaker_core/zaker_tpl_static/wap/tpl_guoji1.html

 

电子行业正面临芯片上晶体管数量的极限。麻省理工学院的工程师们开发了一种新型多层芯片设计,无需硅晶圆基板,且能在较低温度下工作,保护底层电路。该方法直接在彼此之上生长高质量的半导体材料,无需厚硅“地板”,从而实现层间更直接的通信和更快的计算。这种技术有望构建用于人工智能硬件的堆叠芯片,其速度和功率可媲美当今的超级计算机,并能存储与物理数据中心相当的大量数据。这项突破为半导体行业开启了巨大潜力,允许芯片堆叠,并大幅提高人工智能、逻辑和内存应用的计算能力。

🔬 传统芯片制造受限:传统芯片通过在硅晶圆上缩小晶体管尺寸来提高性能,但已接近极限。行业开始探索垂直堆叠多层晶体管的方案,但硅晶圆基板成为主要障碍。

💡 新方法突破:麻省理工学院的工程师们开发出一种无需硅晶圆基板的新型多层芯片设计,通过在彼此之上直接生长高质量的半导体材料,实现层间更直接的通信和更快的计算。

🌡️ 低温生长技术:新方法采用边缘成核技术,在低于400摄氏度的温度下生长单晶二维材料,保护底层电路。这与之前的900摄氏度高温生长方法相比,是一个重大进步。

🔗 多层堆叠应用:该方法成功制造出交替堆叠两种不同二维材料的多层芯片,无需中间硅晶圆,可有效提高芯片的半导体元件密度,并可应用于逻辑电路和内存,实现3D逻辑芯片、3D内存及其组合。

🚀 未来潜力:这项技术有望构建用于人工智能硬件的堆叠芯片,其速度和功率可媲美当今的超级计算机,并能存储与物理数据中心相当的大量数据。该技术已衍生出一家名为FS2的公司,旨在实现商业化。

The electronics industry is approaching a limit to the number of transistors that can be packed onto the surface of a computer chip. So, chip manufacturers are looking to build up rather than out.

Instead of squeezing ever-smaller transistors onto a single surface, the industry is aiming to stack multiple surfaces of transistors and semiconducting elements — akin to turning a ranch house into a high-rise. Such multilayered chips could handle exponentially more data and carry out many more complex functions than today’s electronics.

A significant hurdle, however, is the platform on which chips are built. Today, bulky silicon wafers serve as the main scaffold on which high-quality, single-crystalline semiconducting elements are grown. Any stackable chip would have to include thick silicon “flooring” as part of each layer, slowing down any communication between functional semiconducting layers.

Now, MIT engineers have found a way around this hurdle, with a multilayered chip design that doesn’t require any silicon wafer substrates and works at temperatures low enough to preserve the underlying layer’s circuitry.

In a study appearing today in the journal Nature, the team reports using the new method to fabricate a multilayered chip with alternating layers of high-quality semiconducting material grown directly on top of each other.

The method enables engineers to build high-performance transistors and memory and logic elements on any random crystalline surface — not just on the bulky crystal scaffold of silicon wafers. Without these thick silicon substrates, multiple semiconducting layers can be in more direct contact, leading to better and faster communication and computation between layers, the researchers say.

The researchers envision that the method could be used to build AI hardware, in the form of stacked chips for laptops or wearable devices, that would be as fast and powerful as today’s supercomputers and could store huge amounts of data on par with physical data centers.

“This breakthrough opens up enormous potential for the semiconductor industry, allowing chips to be stacked without traditional limitations,” says study author Jeehwan Kim, associate professor of mechanical engineering at MIT. “This could lead to orders-of-magnitude improvements in computing power for applications in AI, logic, and memory.”

The study’s MIT co-authors include first author Ki Seok Kim, Seunghwan Seo, Doyoon Lee, Jung-El Ryu, Jekyung Kim, Jun Min Suh, June-chul Shin, Min-Kyu Song, Jin Feng, and Sangho Lee, along with collaborators from Samsung Advanced Institute of Technology, Sungkyunkwan University in South Korea, and the University of Texas at Dallas.

Seed pockets

In 2023, Kim’s group reported that they developed a method to grow high-quality semiconducting materials on amorphous surfaces, similar to the diverse topography of semiconducting circuitry on finished chips. The material that they grew was a type of 2D material known as transition-metal dichalcogenides, or TMDs, considered a promising successor to silicon for fabricating smaller, high-performance transistors. Such 2D materials can maintain their semiconducting properties even at scales as small as a single atom, whereas silicon’s performance sharply degrades.

In their previous work, the team grew TMDs on silicon wafers with amorphous coatings, as well as over existing TMDs. To encourage atoms to arrange themselves into high-quality single-crystalline form, rather than in random, polycrystalline disorder, Kim and his colleagues first covered a silicon wafer in a very thin film, or “mask” of silicon dioxide, which they patterned with tiny openings, or pockets. They then flowed a gas of atoms over the mask and found that atoms settled into the pockets as “seeds.” The pockets confined the seeds to grow in regular, single-crystalline patterns.

But at the time, the method only worked at around 900 degrees Celsius.

“You have to grow this single-crystalline material below 400 Celsius, otherwise the underlying circuitry is completely cooked and ruined,” Kim says. “So, our homework was, we had to do a similar technique at temperatures lower than 400 Celsius. If we could do that, the impact would be substantial.”

Building up

In their new work, Kim and his colleagues looked to fine-tune their method in order to grow single-crystalline 2D materials at temperatures low enough to preserve any underlying circuitry. They found a surprisingly simple solution in metallurgy — the science and craft of metal production. When metallurgists pour molten metal into a mold, the liquid slowly “nucleates,” or forms grains that grow and merge into a regularly patterned crystal that hardens into solid form. Metallurgists have found that this nucleation occurs most readily at the edges of a mold into which liquid metal is poured.

“It’s known that nucleating at the edges requires less energy — and heat,” Kim says. “So we borrowed this concept from metallurgy to utilize for future AI hardware.”

The team looked to grow single-crystalline TMDs on a silicon wafer that already has been fabricated with transistor circuitry. They first covered the circuitry with a mask of silicon dioxide, just as in their previous work. They then deposited “seeds” of TMD at the edges of each of the mask’s pockets and found that these edge seeds grew into single-crystalline material at temperatures as low as 380 degrees Celsius, compared to seeds that started growing in the center, away from the edges of each pocket, which required higher temperatures to form single-crystalline material.

Going a step further, the researchers used the new method to fabricate a multilayered chip with alternating layers of two different TMDs — molybdenum disulfide, a promising material candidate for fabricating n-type transistors; and tungsten diselenide, a material that has potential for being made into p-type transistors. Both p- and n-type transistors are the electronic building blocks for carrying out any logic operation. The team was able to grow both materials in single-crystalline form, directly on top of each other, without requiring any intermediate silicon wafers. Kim says the method will effectively double the density of a chip’s semiconducting elements, and particularly, metal-oxide semiconductor (CMOS), which is a basic building block of a modern logic circuitry.

“A product realized by our technique is not only a 3D logic chip but also 3D memory and their combinations,” Kim says. “With our growth-based monolithic 3D method, you could grow tens to hundreds of logic and memory layers, right on top of each other, and they would be able to communicate very well.”

“Conventional 3D chips have been fabricated with silicon wafers in-between, by drilling holes through the wafer — a process which limits the number of stacked layers, vertical alignment resolution, and yields,” first author Kiseok Kim adds. “Our growth-based method addresses all of those issues at once.” 

To commercialize their stackable chip design further, Kim has recently spun off a company, FS2 (Future Semiconductor 2D materials).

“We so far show a concept at a small-scale device arrays,” he says. “The next step is scaling up to show professional AI chip operation.”

This research is supported, in part, by Samsung Advanced Institute of Technology and the U.S. Air Force Office of Scientific Research. 

Fish AI Reader

Fish AI Reader

AI辅助创作,多种专业模板,深度分析,高质量内容生成。从观点提取到深度思考,FishAI为您提供全方位的创作支持。新版本引入自定义参数,让您的创作更加个性化和精准。

FishAI

FishAI

鱼阅,AI 时代的下一个智能信息助手,助你摆脱信息焦虑

联系邮箱 441953276@qq.com

相关标签

多层芯片 半导体 无硅晶圆 低温生长 人工智能硬件
相关文章