Abstract
<jats:p>Capacitor selection and optimization are crucial for power integrity in high-speed electronic systems. However, finding the optimal combination of capacitor values, types, and placements while meeting performance goals is a complex task. This article introduces an AI-based capacitor optimizer that integrates machine learning techniques with circuit and electromagnetic simulators to tackle capacitor optimization more efficiently. The optimizer automates the iterative process of proposing capacitor schemes, simulating their performance, and learning from the results. This method significantly reduces time and costs by streamlining the optimization process. The AI system learns from previous simulations, improving the accuracy and efficiency of capacitor selection. This approach is applicable to various industries, including consumer, automotive, and telecommunications. The AI capacitor optimizer represents a significant advancement in capacitor selection and optimization, allowing engineers to achieve better results in less time and with fewer resources.</jats:p>