Fan-Out Wafer-Level Packaging (FOWLP) achieves heterogeneous chip integration through redistribution layers (RDLs). However, the rapid increase in interconnect density has significantly heightened the topological complexity of net crossings. In light of manufacturing cost constraints and limited routing resources, reducing topological crossings among nets through layer assignment becomes critically important. Unlike existing RDL layer assignment methods that rely on fixed access point circular models, our approach introduces a flexible access point strategy to minimize topological conflicts among nets, thereby reducing the total number of routing layers. The proposed method includes the following key techniques: (1) a multi-directional projection circular (MDPC) model with support for flexible access points to expand the solution space; (2) a global topology optimization strategy based on flexible access points to significantly reduce net crossings; and (3) a wirelength-driven access point allocation algorithm aimed at minimizing total wirelength. Experimental results show that, compared with the fixed access point-based method and the limited flexible access points-based method, the proposed algorithm reduces the number of routing layers by 36.5% and 17.9%, respectively, when the number of layers is not constrained, and increases the number of assigned nets by 19% and 5%, respectively, under fixed-layer constraints.

With the rise of Large Language Models (LLMs), researchers have become increasingly interested in their applications in EDA flows, particularly in specific subdomains such as serving as knowledge assistants and generating RTL code. In this study, we present a Retrieval-Augmented Generation (RAG) framework tailored to EDA task processing, named EDA-Adaptive RAG. This framework addresses the implicit semantics of EDA data and facilitates efficient knowledge acquisition through classification and enhanced retrieval, significantly enhancing LLMs ability to acquire EDA knowledge. Furthermore, we aim to integrate RAG into the design process as an EDA assistant application. Using RTL code generation as a case study, we demonstrate that the performance of RTL code generation can be enhanced through highly relevant retrievals provided by our RAG. The experimental analysis involves EDA Q&A tasks and RTL code generation evaluation. It is shown that our method outperforms the latest works in terms of both answer stability and code quality.

To satisfy the diversity of tactile patterns during Physical Human Robot Interaction(PHRI), this paper proposes a method to recognize human tactile motion using a spherical handle equipped with tactile sensors. The method first exploits convolutional neural networks as universal feature extractors, and then support vector machines are implemented for classifying the 16 kinds of motion in 4D space. Experimental results show the superiority of our approach against other methods, leading to classification rates over 91.19%.