Expedient validation of LED reliability with anomaly detection through multi-output Gaussian process regression

Abstract

The existing LED reliability assessment procedures are costly and time-consuming. It poses even greater challenges for the purchasers to validate the manufacturer test results, prior to goods acceptance. In absence of a simple and expedient validation, purchasers often bear the reliability risk in good faith. Leveraging on the 3-stage early degradation modes, as a signature, for LED under the moisture-electrical-temperature (MET) test, an expedient assessment framework is proposed to validate if the supplied LED batches conform to the contracted reliability. Multi-output Gaussian process regression (MOGPR) is performed on sparse reference data to establish an acceptable threshold for the degradation signature. When regressed against the reference data with contracted reliability, the test LED that displays signature anomaly will be deliberated for rejection. The acceptable threshold is calibrated to minimise false negative rate and false positive rate. The procedure is repeated in accordance with acceptable quality level (AQL) or existing sampling policies to accept or reject the batch. The framework achieves a reduction of the validation time from 6000 h to 100 h. While the initial motivation is to facilitate acceptance by purchasers, manufacturer may also adopt this approach for out-going quality control (QC) or rapid evaluation during design prototyping.