Reliability characterization of Solder Joints in Electronic systems through a Neural Network Aided Approach

Rapid and precise reliability evaluation of electronic circuits plays a key role in the design stage of the electronic systems. The task becomes even more difficult when several major parameters contribute into the reliability evaluation. This paper proposes a neural network aided approach as a prediction tool for estimating the useful lifetime of the ball shaped solder joint as the most resistless part under accidental drops in the electronic devices. Several contributory factors including ball grid array (BGA) chip location, printed circuit board (PCB) thickness, solder alloy composition and solder ball volume are considered in our proposed rapid prediction model and their effects are investigated. 480 finite element simulations as well as 20 experimental tests are performed to obtain an enriched database for our neural network based prediction model. The accuracy of the proposed model is calculated as 97.55% in comparison with the finite element and the experimental results. Ability of considering multi contributory factors in the reliability evaluation of the BGA chip makes our proposed approach be a suitable candidate in design for reliability of the electronic systems.