Manufacturing Variation Modeling and Yield Optimization in Sub-5 nm Semiconductor Technologies

Abstract

Sub-5 nm semiconductor technologies require strict control of manufacturing variation because very small geometry, lithography, and electrical shifts can reduce final yield. This article presents a variation-aware yield optimization framework for advanced transistor manufacturing. The approach connects device geometry variation, EUV lithography stochasticity, wafer-level process drift, electrical parameter spread, and defect distribution with predicted yield behavior. Manufacturing data are cleaned, converted into variation indicators, and used to compare baseline, statistical, and AI-assisted control strategies. The results show that predicted yield decreases as variation level increases, especially when geometry deviation and lithography instability occur together. AI-assisted adaptive control gives the strongest improvement by reducing variation and increasing predicted yield more than baseline and statistical control methods. The framework supports earlier process correction, better variation monitoring, and more reliable yield improvement. It can help manufacturing teams improve process stability in high-volume sub-5 nm semiconductor production with clearer decisions and faster ramp-up outcomes.