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Bias temperature instability represents a significant reliability concern for mosfets, leading to the degeneration of critical operational parameters. Effective identification of path dependence is a prerequisite to ensure that cumulative degradation under the mission profile can be accurately quantified, which involves determining whether the stress interactions affect the degradation. Despite its importance, path dependence is often overlooked, potentially compromising the reliability prediction. This article presents a method for identifying the path dependence that may be inherent to the degradation process, including accelerated degradation test design and path dependence analysis. The practicality and effectiveness of the proposed method are demonstrated through experiments on silicon mosfets, revealing its ability to identify path dependence and provide quantitative evaluation. Empirical results indicate that identical degradation parameters may not necessarily equate to a uniform physical condition of the material. Therefore, it is imperative to exercise increased caution when utilizing traditional models for cumulative degradation computations.

The design optimization of a robust, low-cost, and high-quality permanent magnet actuator (PMA) that meets the requirements of industrial applications presents numerous challenges. In particular, uncertainties arising from fabrication, assembly, and degradation are omnipresent and can produce erratic or infeasible solutions, complicating the robust design optimization (RDO) for PMAs. State-of-art RDO approaches treat these uncertainties as fixed contribution margins while disregarding the changes in the contribution margin, manufacturing cost, and life-cycle quality loss resulting from variations in uncertainty range and time-variant degradation. To address these issues, we propose a heuristic RDO approach for the batch production of PMAs that considers the manufacturing cost, life-cycle quality loss, and adaptive iterative step size to address these issues. Firstly, a unified manufacturing cost model is innovated to assess the complex component morphologies. Subsequently, a life-cycle dynamic quality loss model considering the time-variant degradation is established by applying the modified quality loss function, which can effectively track the losses caused by fluctuations in life-cycle quality. Based on the manufacturing cost and life-cycle quality loss models, a heuristic RDO approach with an adaptive iterative step size model is proposed, which automatically assigns the maximum bound of uncertainties and reduces the quality variation without significantly increasing manufacturing cost. Finally, a case study on a latching rotary PMA is used to illustrate the effectiveness of the proposed approach through simulation and experiment. This method achieves a 35.8% increase in robustness for batch-produced PMAs along with a 100% reduction in life-cycle quality losses at only a marginal increase of 5.9% in manufacturing costs.

Dependent degradation processes of performance characteristics are ubiquitous in engineered systems. The initial state of each degradation process is usually random and relates to the degradation rate. Unfort unately, most existing Wiener-based bivariate degradation models fail to consider these two features, which may limit the accuracy of system reliability assessment. In order to surmount this limitation, in this article, we develop a new bivariate degradation model, which involves a generalized Wiener process-based marginal degradation model that considers the random initial state and the correlation between the initial state and the degradation rate and a corresponding degradation increment-based dependence structure (DS). The proposed bivariate degradation model and its reliability function are derived first. Then, a two-stage statistical inference is introduced using the maximum likelihood estimation method. Furthermore, a simulation study investigates the performance of the statistical inference and the misspecification effects of DSs and marginal degradation models. Finally, an illustrative example demonstrates the effectiveness of the proposed model.