Prediction of the Linearity of Analog-to-Digital Converters Exposed to Radiation

This study evaluates the applicability of the Advanced Predictor of Electrical Parameters (APEP) methodology to predict the degradation of key electrical parameters in analog-to-digital converters (ADCs) exposed to ionizing radiation, from measurements performed on the non-radiated device. While the APEP method has previously been validated for discrete analog devices, its extension to complex mixed-signal components has not yet been explored. This work addresses this extension using the PRECEDER database. The APEP methodology, based on machine learning techniques, is enhanced through multivariable analysis tools. This study focuses on the Integral Non-Linearity (INL) parameter of the AD574 converter, widely utilized in the aerospace applications. The results demonstrate that the APEP method can be successfully extended to ADCs, improving prediction performance with the incorporation of multiple electrical parameters which are non-radiated measurements. This new improvement is supported by t-Distributed Stochastic Neighbour Embedding (t-SNE), used as an exploratory analysis to reveal non-linear relationships among parameters that are not evident through univariate analysis. Overall, these findings confirm the potential of the multivariable APEP method to reduce the need for costly and destructive radiation testing, contributing to lower validation costs in space environments.