arXiv subjects

Yongjian Xu

Publications and source records attributed to Yongjian Xu.

5 recordsLinked to original sources

arXiv2025

Image2Net: Datasets, Benchmark and Hybrid Framework to Convert Analog Circuit Diagrams into Netlists

Large Language Model (LLM) exhibits great potential in designing of analog integrated circuits (IC) because of its excellence in abstraction and generalization for knowledge. However, further development of LLM-based analog ICs heavily relies on textual description of analog ICs, while existing analog ICs are mostly illustrated in image-based circuit diagrams rather than text-based netlists. Converting circuit diagrams to netlists help LLMs to enrich the knowledge of analog IC. Nevertheless, previously proposed conversion frameworks face challenges in further application because of limited support of image styles and circuit elements. Up to now, it still remains a challenging task to effectively convert complex circuit diagrams into netlists. To this end, this paper constructs and opensources a new dataset with rich styles of circuit diagrams as well as balanced distribution of simple and complex analog ICs. And a hybrid framework, named Image2Net, is proposed for practical conversion from circuit diagrams to netlists. The netlist edit distance (NED) is also introduced to precisely assess the difference between the converted netlists and ground truth. Based on our benchmark, Image2Net achieves 80.77% successful rate, which is 34.62%-45.19% higher than previous works. Specifically, the proposed work shows 0.116 averaged NED, which is 62.1%-69.6% lower than state-of-the-arts. Our datasets and benchmark are available at https://github.com/LAD021/ci2n_datasets.

cs.AR↗

arXiv2023

A Distribution Evolutionary Algorithm for the Graph Coloring Problem

Graph coloring is a challenging combinatorial optimization problem with a wide range of applications. In this paper, a distribution evolutionary algorithm based on a population of probability model (DEA-PPM) is developed to address it efficiently. Unlike existing estimation of distribution algorithms where a probability model is updated by generated solutions, DEA-PPM employs a distribution population based on a novel probability model, and an orthogonal exploration strategy is introduced to search the distribution space with the assistance of an refinement strategy. By sampling the distribution population, efficient search in the solution space is realized based on a tabu search process. Meanwhile, DEA-PPM introduces an iterative vertex removal strategy to improve the efficiency of $k$-coloring, and an inherited initialization strategy is implemented to address the chromatic problem well. The cooperative evolution of the distribution population and the solution population leads to a good balance between exploration and exploitation. Numerical results demonstrate that the DEA-PPM of small population size is competitive to the state-of-the-art metaheuristics.utes to its competitiveness to the state-of-the-art metaheuristics.

cs.NE↗

arXiv2023

Scalable Multiple Patterning Layout Decomposition Implemented by a Distribution Evolutionary Algorithm

As the feature size of semiconductor technology shrinks to 10 nm and beyond, the multiple patterning lithography (MPL) attracts more attention from the industry. In this paper, we model the layout decomposition of MPL as a generalized graph coloring problem, which is addressed by a distribution evolutionary algorithm based on a population of probabilistic model (DEA-PPM). DEA-PPM can strike a balance between decomposition results and running time, being scalable for varied settings of mask number and lithography resolution. Due to its robustness of decomposition results, this could be an alternative technique for multiple patterning layout decomposition in next-generation technology nodes.

cs.NE↗

arXiv2021

Effects of resonant magnetic perturbations on neutral beam heating in a tokamak

Effects of resonant magnetic perturbations (RMPs) on tangential neutral beam heating in the EAST tokamak are studied numerically. RMPs with linear resistive magnetohydrodynamics response are used in the modeling. A variety of representing configurations of RMP coil currents are examined and their effects on the NBI heating efficiency are compared, in order to find a parameter window where deleterious effects of RMPs on NBI heating efficiency are minimized. It is found that the internal redistribution of fast ions by RMPs induces local accumulation of fast ions, resulting in higher local fast ion pressure than the case without RMPs. It is also found that the toroidal phasing of the RMP with respect to the fast ion source has slight effects on the steady-state radial profile of fast ions. The dependence of fast ion loss fraction on the RMP up-down phase difference shows similar behavior as the dependence of the radial width of chaotic magnetic field on the phase difference. A statistical method of identifying resonances between RMPs and lost fast ions is proposed and the results indicate that some resonances between RMPs and lost passing particles may be of non-integer fractional order, rather than the usual integer order.

physics.plasm-ph↗

arXiv2021

A Cuckoo Quantum Evolutionary Algorithm for the Graph Coloring Problem

Based on the framework of the quantum-inspired evolutionary algorithm, a cuckoo quantum evolutionary algorithm (CQEA) is proposed for solving the graph coloring problem (GCP). To reduce iterations for the search of the chromatic number, the initial quantum population is generated by random initialization assisted by inheritance. Moreover, improvement of global exploration is achieved by incorporating the cuckoo search strategy, and a local search operation, as well as a perturbance strategy, is developed to enhance its performance on GCPs. Numerical results demonstrate that CQEA operates with strong exploration and exploitation abilities, and is competitive to the compared state-of-the-art heuristic algorithms.

cs.NE↗