An Analytical Approach to Calculate Soft Error Rate Induced by Atmospheric Neutrons

In the atmosphere, it is generally understood that neutrons are the main contributor to the soft error rate (SER) in electronic devices. These particles are indeed able to trigger nuclear reactions in the sensitive regions of the devices, leading to secondary ions that may ionize the matter sufficiently to upset a memory cell or induce a transient signal, known as soft errors. For reliability purposes, it is crucial to be able to estimate the SER associated with a given technology, which is typically characterized by its sensitive volume and its threshold linear energy transfer (LET). As an alternative to the usual Monte Carlo methods, in this work we present an analytical model for SER prediction, where we separate the radiation-matter interaction from the geometry considerations (sensitive volume). By doing so, we show that the SER can be expressed as the sum of two contributions that can be calculated for any threshold LET and any sensitive volume size. We compare our proposed approach to existing Monte Carlo simulations in the literature, obtaining a very good agreement despite our approximations, thus validating our approach. As an additional result, we can show that, for future down-sized technologies that may be more sensitive to radiation effects, the contribution of neutrons in the 1-10 MeV energy range to the SER is expected to decrease.

Automated summary

In this study, an analytical model for predicting the soft error rate (SER) is proposed. The model separates the radiation-matter interaction from the geometry considerations, allowing the SER to be expressed as the sum of two contributions that can be calculated for any threshold linear energy transfer and any sensitive volume size. The proposed approach is validated by comparing it to Monte Carlo simulations, with good agreement despite approximations. Furthermore, the study shows that the contribution of neutrons in the 1-10 MeV energy range to the SER is expected to decrease for future down-sized technologies.