From Barrier-Centric Leakage to Structural Statistical Variability in Ultra-Scaled Devices

Leakage mechanisms in advanced semiconductor nodes are increasingly governed by localized structural variability, including line-edge roughness (LER), stochastic geometry fluctuations, interface states, and localized field concentrations. As dimensions scale toward physical limits, these perturbations induce tail-sensitive leakage distributions that are difficult to capture using conventional average-barrier electrostatic abstractions. This work introduces a structural statistical abstraction framework based on the concept of realizable low-cost conduction configurations. Instead of explicitly resolving the full spectrum of microscopic transport paths, the proposed framework statistically aggregates localized spatial conduction behavior into a compact multiplicity descriptor denoted as N_eff. An algorithmic post-processing workflow is further established to demonstrate how localized conduction configurations may be isolated from TCAD-derived spatial distributions using density-based clustering methods. The framework is intended to operate as a lightweight abstraction layer positioned between detailed transport physics and higher-level engineering workflows, including variability-aware analysis, leakage-tail characterization, yield screening, and design-technology co-optimization (DTCO). The central argument of this work is modest but practical: advanced-node leakage variability increasingly requires an intermediate structural statistical abstraction layer capable of compressing high-dimensional distributed physical fields into computationally manageable engineering metrics.