Machine learning-driven multi-objective parameter optimization for sustainable, efficient, and high-quality ultrasonic wire bonding

Abstract Ultrasonic wire bonding, a highly automated production process, finds extensive use in the electronics and electromobility sectors, with billions of applications annually. Wire bonding, a critical step in electrical manufacturing, demands high quality while rising energy costs push industries to improve efficiency. The complexity of the process and the multitude of non-linear influencing parameters in the bonding process make it difficult for engineers to quickly find the optimum parameter sets for multiple response problems simultaneously solely based on their experience. As a result, engineers commonly resort to iterative trial and error approaches to establish wire bond parameters in practice. This paper introduces a novel, machine-learning-based methodology using established optimization algorithms for automated multi-objective parameter optimization in ultrasonic copper wire bonding, considering ten key parameters that influence the normal force profile, ultrasonic power profile, and process duration. The novelty of the proposed method lies in its ability to improve process sustainability by reducing energy input and tool wear, while simultaneously maximizing bond quality (shear force) and minimizing process time, without the need for a physical model or prior process knowledge. The paper shows that the combination of Bayesian optimization with artificial neural networks is particularly effective, achieving a 3.7% reduction in energy input and a 14.4% reduction in process time, while maintaining bond quality and reducing tool wear. This approach proves to be faster, less resource-intensive, and more effective than manual optimization methods, offering a scalable solution for industrial use.