As an important intermediate between integrated circuits (ICs) and the printed circuit board (PCB), the routing in the package substrate plays a crucial role in the efficiency and accuracy of signal and power transmis...
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As an important intermediate between integrated circuits (ICs) and the printed circuit board (PCB), the routing in the package substrate plays a crucial role in the efficiency and accuracy of signal and power transmission. While numerous research efforts have focused on substrate routing to avoid inefficient, time-consuming, and error-prone manual processes, few of them have addressed the challenge of routing multi-pin nets, particularly those with a large number of pins. This paper presents a three-stage framework of multi-pin net routing for packages with fine-pitch ball grid arrays, consisting of pin grouping, minimum spanning tree topology generation, and group topology connection. Our framework classifies net connections into different categories, prioritizes their routing ordering, and applies different routing approaches and strategies to boost overall routability. The results of the experiments conducted on six real industrial designs demonstrate that our framework can simultaneously and effectively handle two-pin nets and multi-pin nets with better routing performance compared to the state-of-the-art work.
Deconvolution of relationships between bacterial artificial chromosome (BAC) clones and genes is a crucial step in the selective sequencing of regions of interest in a genome. It often includes combinatorial pooling o...
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Deconvolution of relationships between bacterial artificial chromosome (BAC) clones and genes is a crucial step in the selective sequencing of regions of interest in a genome. It often includes combinatorial pooling of unique probes obtained from the genes (unigenes), and screening of the BAC library using the pools in a hybridization experiment. Since several probes can hybridize to the same BAC, in order for the deconvolution to be achievable the pooling design has to be able to handle a large number of positives. As a consequence, smaller pools need to be designed, which in turn increases the number of hybridization experiments, possibly making the entire protocol unfeasible. We propose a new algorithm that is capable of producing high-accuracy deconvolution even in the presence of a weak pooling design, i.e. when pools are rather large. The algorithm compensates for the decrease of information in the hybridization data by taking advantage of a physical map of the BAC clones. We show that the right combination of combinatorial pooling and our algorithm not only dramatically reduces the number of pools required, but also successfully deconvolutes the BAC-gene relationships with almost perfect accuracy. Software is available on request from the first author.
The Multi-Criteria Test Suite Minimization (MCTSM) problem aims to remove redundant test cases, guided by adequacy criteria such as code coverage or fault detection capability. However, current techniques either exhib...
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The Multi-Criteria Test Suite Minimization (MCTSM) problem aims to remove redundant test cases, guided by adequacy criteria such as code coverage or fault detection capability. However, current techniques either exhibit a high loss of fault detection ability or face scalability challenges due to the NP-hard nature of the problem, which limits their practical utility. We propose TripRL, a novel technique that integrates traditional criteria such as statement coverage and fault detection ability with test coverage similarity into an integerlinear Program (ILP), to produce a diverse reduced test suite with high test effectiveness. TripRL leverages bipartite graph representation and its embedding for concise ILP formulation and combines ILP with effective reinforcement learning (RL) training. This combination renders large-scale test suite minimization more scalable and enhances test effectiveness. Our empirical evaluations demonstrate that TripRL’s runtime scales linearly with the magnitude of the MCTSM problem. Notably, for large test suites from the Defects4j dataset where existing approaches fail to provide solutions within a reasonable time frame, our technique consistently delivers solutions in less than 47 minutes. The reduced test suites produced by TripRL also maintain the original statement coverage and fault detection ability while having a higher potential to detect unknown faults.
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