We develop photolithography equipment and computational tools for mask optimization in step-and-scan architectures, from mature DUV nodes to leading-edge EUV.

Focus

• Reticle optimization — computational correction of mask patterns to compensate for diffraction, lens aberrations, and stage-induced overlay error at the exposure field.
• Trajectory generation — higher-order motion profiles for wafer and reticle stages that minimize residual vibration and shorten settling time, increasing throughput without degrading critical-dimension fidelity.

Context

Monterrey sits at the center of a growing semiconductor supply chain in northeastern México, with automotive, industrial, and power-management demand driving sustained volume across a wide range of nodes. MNNW was founded to bring locally developed equipment and optimization capability to that ecosystem — a region where access to specialized tooling and the expertise to adapt it has historically been limited.

Research

The throughput–precision trade-off in step-and-scan systems is governed by how aggressively the stage can be moved between exposures. Recent work in the field has converged on adaptive control and higher-order input shaping as the most productive approaches to that problem. Our mask optimization pipeline is designed around the overlay error budgets that result from those trajectory constraints — so that reticle corrections remain valid even as scanner throughput increases.