The Atomica AI Optical Source Platform is a modular microfabrication platform for the precision structures that move the optical source closer to compute — for compact laser sources, optical engines, fiber coupling, and thermally stable AI optical interconnect. As AI clusters grow larger, denser, and power-hungry, the laser source is a distinct challenge: too hot for the compute package, yet needing micron-precise coupling. Atomica doesn’t build your lasers, GPUs, or transceivers; it manufactures the enabling structures that make optical integration practical, manufacturable, and scalable — silicon optical benches, V-grooves, microlenses, micromirrors, thermal structures, wafer-level packaging, and through-glass vias — from prototype to volume production.

What you will find in this guide

  • Applications of the AI Optical Source Platform — The system-level architectures it supports: external laser source modules, optical engines, laser-to-fiber coupling, near-packaged optics, and co-packaged optics for AI data centers.
  • The function of the AI Optical Source Platform — How the platform generates, stabilizes, routes, and couples light efficiently as one physical layer between photonic devices and deployable AI hardware, from the laser die to the fiber.
  • Common Block Library — The reusable microfabrication building blocks — silicon optical benches, V-grooves, microlenses, micromirrors, laser submounts, thermal isolation structures, wafer-level packaging, and through-glass vias — combined and adapted to each customer architecture.
  • Advantages of the AI Optical Source Platform — Why a modular, platform-based approach shortens development time, reduces process risk, improves manufacturability, and provides a clear, structured path from concept to scalable manufacturing.
  • Fabrication considerations of the AI Optical Source Platform — The optical, mechanical, electrical, thermal, and manufacturability tradeoffs that determine coupling efficiency, wavelength stability, yield, reliability — and whether a design can scale.