A modern chip begins as refined silicon and ends as billions of features printed at scales smaller than the wavelength of light. At that edge, physics turns probabilistic — identical patterns print perfectly in one place and fail a few nanometers over, not from any design flaw, but from the statistics of individual photons and molecules. These stochastic failures are the dominant factor deciding yield at advanced nodes, and today the industry finds them the hard way.
A modern chip begins as refined silicon and ends as billions of features printed at scales smaller than the wavelength of light. At that edge, physics turns probabilistic. Identical patterns print perfectly in one place and fail a few nanometers over, not from any design flaw, but from the statistics of individual photons and molecules. These stochastic failures are the dominant factor deciding yield at advanced nodes, and today the industry finds them the hard way.
Stochastic simulation today runs at clip scale. Rigorous physics models resolve a few features over hours of compute, calibrated to a resist and process that has already been characterized. A chip has billions of features, and the decisions that matter most are made before that data exists.
Senran computes stochastic failure probability across a full layout. Every feature receives a failure probability and the physical mechanism driving it, from photon statistics and electron cascade to acid diffusion and mask CD error, at the scale of a chip and the speed of a decision.
Every constant is derived from first-principles physics rather than fitted to wafer data. The prediction therefore extends to processes without silicon, resists without datasets, and nodes that have not been run.