Documenting the trail as we blaze it

Follow CDimension’s journey as we engineer to scale the next computing frontier. Our blog shares how we’re rethinking chip design from the ground up to help power tomorrow’s AI, robotics, and cloud.

Vision

From Lab Curiosity to Industry-Ready: How CDimension is Making 2D Materials Work for the Future of Electronics

In recent years, 2D materials have garnered intense academic interest for their extraordinary electrical, optical, mechanical, and quantum properties. From graphene to transition metal dichalcogenides (TMDs) like MoS₂ and WSe₂, and insulating materials like h-BN, the range of possible applications spans transistors, photonics, display technologies, quantum computing, and beyond.

Technology

The Future of AI Hardware: Breaking the Silicon Barrier

The AI revolution has put an enormous strain on hardware. For decades, progress in computing was driven by making silicon transistors ever smaller (Moore's Law), but today we are hitting fundamental limits. Chip designers face scaling bottlenecks, power limits, and density constraints as 2D silicon chips approach their physical and economic limits.

Technology

Beyond Silicon: Why MoS₂ Matters in AI Hardware

Rethinking Semiconductor Materials: Silicon has been the workhorse of the semiconductor industry for over half a century, but as we push into the nanoscale era, new materials are gaining attention. One of the most promising is Molybdenum Disulfide (MoS₂), a two-dimensional (2D) material. Unlike silicon, which is a bulk 3D crystal, MoS₂ can form stable crystalline sheets just a single atom thick (with a molybdenum layer sandwiched between sulfur atoms).

Vision

Revolutionizing Power Management with 3D Integration

Power management components (capacitors, inductors, etc.) on a typical circuit board. In advanced systems, such bulky components occupy significant space and contribute to power losses. Power management is the unsung hero (or sometimes the villain) in high-performance computing.

Vision

Breaking Memory Bottlenecks in AI

AI computation isn’t just about crunching numbers – it’s also about moving data. A well-known challenge in computer architecture is the “memory wall”: processor speeds have increased much faster than memory speeds, so processors often sit idle waiting for data to come from memory. In AI workloads, this is exacerbated by the huge size of neural network models and the datasets they process. An AI chip might perform tens of trillions of operations per second, but if it can’t get data in and out of memory quickly enough, those ALUs (arithmetic logic units) are starving.

Want to know more? Stay in touch

See what we’re working on, be the first to hear about product releases, and get our insights articles sent right to your inbox.

Thank you! Your submission has been received!

Oops! Something went wrong while submitting the form.