Application of generalized linear models for optimizing production stress testing

Abstract

Accelerated environmental stress tests (EST) are applied during the manufacturing process to improve reliability by precipitating and detecting latent defects. This test represents an in-process manufacturing screen and the objective of performing it is to avoid early field failures that reduce the customer satisfaction level and increase warranty and compensation costs. Temperature cycling during EST is one of the most commonly used test procedures. Although it is an expensive and energy intensive procedure, usually a lengthy test is initially recommended for a new product. Based on the product test performance or a possible manufacturing process modification, the test duration and regime may be changed after some period. Even if the number of test cycles is reduced, EST continues to be an expensive test and a major process bottleneck. This paper uses generalized linear modeling (GLM) to investigate the effects of the production and EST test variables on the population under test. Both the number of units rejected and the time to failure can be modeled as a regression function of covariates representative of the test environment. The field reliability function is written as a product of the unconditional reliability in each segment of the test profile such as dwell, ramp, etc. The next step is to apply the result of the temperature cycle EST GLM to a mathematical cost model. This cost model includes both the test cost and the warranty and compensation costs of the early field failures. The optimum test regime and number of cycles, which minimizes the total cost is determined by combining the GLM and the cost model. In this way the production test regime can be optimized in terms of field reliability/test cost trade-off.