Reliability Inference in GLFP Models Based on EM Algorithm With Related Application

Abstract

During the manufacturing processes for the integrated circuit (IC) products, defective units may not be screened out by the quality inspections. The defective units often lead to infant mortality failure in the early stages of operation, while non-defective units will eventually fail due to wear-out failure. The general limited failure population (GLFP) model can be used to describe such a phenomenon in which defective units induce failure affected by both failure mechanisms, but failure of non-defective units is only due to wear-out. Besides, when a failure occurs, it is not known whether it is defective and yet which failure mode causes the failure. This article proposes an EM algorithm along with the missing information principle for the GLFP models under multiply censored Weibull distributions to simplify the maximum likelihood (ML) inference. It resolves the computational instability and provides more accurate reliability inference. With the embedded latent variables, failure mode detection and defect identification are also made for masked data, consequently. Furthermore, the proposed method can be extended to the GLFP models of interval data. The simulation study shows that the proposed method provides more accurate results. Two illustrative examples highlight the feasibility and advantages of the proposed approach.