Design Optimization Study by AI-Surrogate PBGA Reliability Model and Transfer Learning

Solder joint reliability remains an important design issue for ball grid array (BGA) type packaging. Although the finite element model is used to optimize the solder joint reliability, the finite element method's deterministic nature limits the generation of statistical information. In this paper, the mechanical characteristics of the solder joint of PBGA packaging are represented by a validated and simplified finite model. We then explored an AI-surrogate model to capture the solder joint fatigue characteristics of PBGA packaging and then applied the same surrogate for design optimization and process uncertainty. Various techniques are integrated, including finite model simplification and validation methods, AI training methods, AI model optimization, and statistical uncertainty assessment methods. The result shows the seamless integration of all the above-mentioned methods. Moreover, when different AI-surrogate models are applied, stable optimization results can be expected when the proper AI-surrogate model training criterion can be met. Furthermore, the transfer learning technique is applied to obtain a revised NN model against the new database, and its prediction capability is demonstrated.