An Application of Semiparametric Bayesian Isotonic Regression to the Study of Radiation Effects in Spaceborne Microelectronics

• Activity Number: 64
• Type: Topic Contributed
• Date/Time: Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Marian Farah*+ and Athanasios Kottas and Robin D. Morris
• Companies: University of California, Santa Cruz and University of California, Santa Cruz and Universities Space Research Association
• Keywords: Dirichlet process prior ; Isotonic regression ; Single Event Upset ; Cross-section vs. LET curve

Abstract

This work is concerned with the vulnerability of spaceborne microelectronics to single event upset (SEU), a change of state caused by electromagnetic radiation striking a sensitive node. To measure the susceptibility of a device to SEU, testing is conducted in a particle accelerator. The number of upsets depends on the linear energy transfer (LET), the cross-section (CS) of interaction, and the fluence. The interaction CS is assumed to be monotonically increasing with LET. Standard practice in the device testing literature is to assume a parametric form for the CS vs. LET curve. We propose a semiparametric isotonic regression method using a Poisson model for the upset counts and a Dirichlet process prior for the CS vs. LET curve. This allows the data to drive the shape of the CS vs. LET relationship, and can thus result in more accurate predictive inference for the on-orbit upset rate.