Quality Control of Lifetime Drift in Discrete Electrical Parameters in Semiconductor Devices via Transition Modeling

TL;DR

The document describes elsarticle.cls, a LaTeX class tailored for Elsevier submissions that extends the base article framework to minimize package conflicts and provide consistent formatting. It highlights design choices that reduce clashes with common tools, adds configurable submission formats (preprint, final, and model-specific styles), and enhances front matter and theorem-like environments. The paper emphasizes installation, usage, and compatibility with widely used packages (natbib, geometry, hyperref, endfloat), enabling a streamlined workflow for authors. Overall, elsarticle.cls aims to improve reliability, configurability, and compatibility for Elsevier manuscript preparation across platforms.

Abstract

Semiconductors are widely used in various applications and critical infrastructures. These devices have specified lifetimes and quality targets that manufacturers must achieve. Lifetime estimation is conducted through accelerated stress tests. Electrical parameters are measured at multiple times during a stress test procedure. The change in these Electrical parameters is called lifetime drift. Data from these tests can be used to develop a statistical model predicting the lifetime behavior of the electrical parameters in real devices. These models can provide early warnings in production processes, identify critical parameter drift, and detect outliers. While models for continuous electrical parameters exists, there may be bias when estimating the lifetime of discrete parameters. To address this, we propose a semi-parametric model for degradation trajectories based on longitudinal stress test data. This model optimizes guard bands, or quality guaranteeing tighter limits, for discrete electrical parameters at production testing. It is scalable, data-driven, and explainable, offering improvements over existing methods for continuous underlying data, such as faster calculations, arbitrary non-parametric conditional distribution modeling, and a natural extension of optimization algorithms to the discrete case using Markov transition matrices.