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An application of semiparametric Bayesian isotonic regression to the study of radiation effects in spaceborne microelectronics
Author(s) -
Farah Marian,
Kottas Athanasios,
Morris Robin D.
Publication year - 2013
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2012.01052.x
Subject(s) - upset , single event upset , linear energy transfer , cross section (physics) , physics , computational physics , radiation , computer science , nuclear physics , statistics , mathematics , computer hardware , static random access memory , quantum mechanics
Summary. This work is concerned with the vulnerability of spaceborne microelectronics to single‐event upset, which is a change of state caused by high‐energy charged particles in the solar wind or the cosmic ray environment striking a sensitive node. To measure the susceptibility of a semiconductor device to single‐event upsets, testing is conducted by exposing it to high‐energy heavy ions or protons produced in a particle accelerator. The number of upsets is characterized by the interaction cross‐section, which is an increasing function of linear energy transfer. The prediction of the on‐orbit upset rate is made by combining the device geometry and cross‐section versus linear energy transfer curve with a model for the orbit‐specific radiation environment. We develop a semiparametric isotonic regression method for the upset count responses, based on a Dirichlet process prior for the cross‐section curve. The methodology proposed allows the data to drive the shape of the cross‐section versus linear energy transfer relationship, resulting in more robust predictive inference for the on‐orbit upset rate than conventional models based on Weibull or log‐normal parametric forms for the cross‐section curve. We illustrate the modelling approach with data from two particle accelerator experiments.