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.
At advanced nodes, yield is decided at design time and discovered in the fab, months later. Senran closes that distance. Every feature in a layout receives a failure probability while the layout can still change, so risk becomes something designers remove, not something fabs find.
Semiconductor design is entering an era where AI makes more of the decisions. Layouts will be generated, evaluated, and revised at machine speed, and the physics of manufacturing will not wait for human review. The question at every step, whether asked by an engineer or an agent, is the same: will this pattern survive the wafer. Senran exists to answer it, at any point in the flow, at the speed the decision is made.