A Knowledge Based Method for Optimization of Decoupling Capacitors in Power Delivery Networks

A Novel Physics-Assisted Genetic Algorithm for Decoupling Capacitor Optimization

“…Recently, with the popularization of artificial intelligence, machine learning (ML)-based methods, such as reinforcement learning (RL) [11], [12], [13], [14], have been broadly adopted in PI optimization problems. Besides, some algorithms based on human experience and knowledge have also been proposed to quickly determine the decap distribution, such as the Newton-Hessian minimization method [15] and several other approaches [16], [17], [18], [19], [20] with different empirical knowledge and decision-making rules.…”

“…[11], [12], [13], [14] require a significant amount of time for data generation and model training, and the robustness and generalization performance of the RL models are difficult to ensure. The human-knowledge-inspired methods [15], [16], [17], [18], [19], [20] can find feasible decap solutions, but the solution quality cannot be guaranteed for large-scale scenarios. Besides, some methods mentioned above [13], [20] can only optimize either the selection of decap types or the decap locations, but not simultaneously.…”

“…The human-knowledge-inspired methods [15], [16], [17], [18], [19], [20] can find feasible decap solutions, but the solution quality cannot be guaranteed for large-scale scenarios. Besides, some methods mentioned above [13], [20] can only optimize either the selection of decap types or the decap locations, but not simultaneously. Moreover, most of these methods assume that all decap types can be placed at each candidate location; however, in real designs, there are usually different constraints on decap sizes at different decap locations.…”

Stochastic Time-Domain Mapping for Comprehensive Uncertainty Assessment in Eye Diagrams

“…Many of the first successful approaches were based on polynomial chaos expansion (for an overview of this family of algorithms see [3]). These were followed by other machine learning inspired solutions falling mainly into the category of kernel machine regression (e.g., [4], [5], [6], [7], [8], [9]) and artificial neural networks (ANNs) (e.g., [10], [11], [12], [13], [14]). This article focuses on kernel machine regression techniques, such as: the support vector machine (SVM) regression [15], the least-squares support vector machine (LS-SVM) [16] regression, and the more recent vector-valued kernel ridge regression (KRR) [6], [7], [17], which have been proven particularly effective for various microelectronics and radio-frequency applications [4], [5], [8].…”