Determination of Bond Wire Failure Probabilities in Microelectronic Packages

TL;DR

This paper presents a hybrid Monte Carlo method combining expensive models with surrogates to efficiently estimate bond wire failure probabilities in microelectronic packages under uncertain manufacturing tolerances and operating conditions.

Contribution

It introduces a non-intrusive, iterative hybrid Monte Carlo scheme that maximizes surrogate evaluations for accurate failure probability estimation with reduced computational cost.

Findings

Significant reduction in computational effort compared to standard Monte Carlo methods.

Successful application to a real microelectronic package example.

Quantitative assessment of surrogate efficiency improvements.

Abstract

This work deals with the computation of industry-relevant bond wire failure probabilities in microelectronic packages. Under operating conditions, a package is subject to Joule heating that can lead to electrothermally induced failures. Manufacturing tolerances result, e.g., in uncertain bond wire geometries that often induce very small failure probabilities requiring a high number of Monte Carlo (MC) samples to be computed. Therefore, a hybrid MC sampling scheme that combines the use of an expensive computer model with a cheap surrogate is used. The fraction of surrogate evaluations is maximized using an iterative procedure, yielding accurate results at reduced cost. Moreover, the scheme is non-intrusive, i.e., existing code can be reused. The algorithm is used to compute the failure probability for an example package and the computational savings are assessed by performing a surrogate efficiency study.