Some wafer fabrication processes are repeated processes, e.g. atomic layer deposition (ALD) process. For such processes, the wafers need to visit some processing modules for a number of times, which complicates the cy...
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Some wafer fabrication processes are repeated processes, e.g. atomic layer deposition (ALD) process. For such processes, the wafers need to visit some processing modules for a number of times, which complicates the cycle time analysis. This paper studies the cycle time analysis problem for such processes. With a Petri net model, it is found that such processes contain local cycles involving only the revisiting PMs and global cycles involving both revisiting and non-revisiting PMs. The process switches between these two types of cycles such that the process never reaches a steady state. Based on this finding, the mechanism underlying such processes is revealed and analytical expressions are given for the calculation of their cycle time. Illustrative examples are presented to show the application of the proposed approach.
With wafer revisit, it is complicated to schedule cluster tools in semiconductor fabrication. In wafer fabrication processes, such as atomic layer deposition (ALD), the wafers need to visit some process modules for a ...
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With wafer revisit, it is complicated to schedule cluster tools in semiconductor fabrication. In wafer fabrication processes, such as atomic layer deposition (ALD), the wafers need to visit some process modules for a number of times. The existing swap-based strategy can be used to operate a dual-arm cluster tool for such a process. It results in a 3-wafer cyclic schedule. However, it is not optimal in the sense of cycle time. Thus, to search for a better schedule, a Petri net model is developed for a dual-arm cluster tool with wafer revisit. With it, the properties of the 3-wafer schedule are analyzed. It is found that, to improve the performance, it is necessary to reduce the number of wafers completed in a cycle. Thus, a 1-wafer schedule is developed by using a new swap-based strategy.
Real-time multi-target path planning is a key issue in the field of autonomous driving. Although multiple paths can be generated in real-time with polynomial curves, the generated paths are not flexible enough to deal...
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The ability of road networks to withstand external disturbances is a crucial measure of transportation system performance, where resilience distinctly emerges as an effective perspective for its unique insights into t...
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We study the multicast capacity for hybrid wireless networks consisting of ordinary wireless nodes and base stations under Gaussian Channel model, which generalizes both the unicast capacity and broadcast capacity for...
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ISBN:
(纸本)9781424444816
We study the multicast capacity for hybrid wireless networks consisting of ordinary wireless nodes and base stations under Gaussian Channel model, which generalizes both the unicast capacity and broadcast capacity for hybrid wireless networks. We simply consider the hybrid extended network, where the ordinary wireless nodes are placed in the square region A(n) with side-length n~(1/2) according to a Poisson point process with unit intensity. In addition, m additional base stations (BSs) serving as the relay gateway are placed regularly in the region A(n) and they are connected by a high-bandwidth wired network. Three broad categories of multicast strategies are proposed in this paper. According to the different scenarios in terms of m, n and n_d, we select the optimal scheme from the three categories of strategies, and derive the achievable multicast throughput based on the optimal decision.
Brain storm optimization (BSO) is a newly proposed population-based optimization algorithm which uses a logarithmic sigmoid transfer function to adjust its search range during the convergent process. However, this adj...
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The ability of road networks to withstand external disturbances is a crucial measure of transportation system performance, where resilience distinctly emerges as an effective perspective for its unique insights into t...
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The ability of road networks to withstand external disturbances is a crucial measure of transportation system performance, where resilience distinctly emerges as an effective perspective for its unique insights into the system's resistance and recovery capabilities. In the face of unforeseen resilience disturbance events, predictive and accurate assessment of road network resilience is essential for better traffic regulation and emergency response management. However, existing resilience assessment methods of road networks are insufficient: they lack reliable real-time big-data analysis, do not possess predictive capabilities for guiding decision-making, and have a narrow view with single-dimensional resilience indicators. To address these issues, focusing on rainfall disturbance scenarios, this work introduces a novel resilience assessment method, which is predictive and real-time, consisting of two components: a deep learning traffic indicator prediction model and a comprehensive resilience assessment model. Firstly, we propose a two-stage traffic indicator prediction model, namely the Conditional Diffusion-Reconstruction-based Graph Neural Network (CDRGNN), which particularly enhances disturbance-scenario prediction accuracy, thereby providing reliable foresight in aid of the following assessments. Subsequently, we develop a resilience assessment model featuring structural-functional comprehensive resilience indicators established through shortest-path aggregation, and the overall resilience assessment is performed through comparative analysis using indicators obtained in real-time with historical non-disruptive resilience benchmarks. In a case study focusing on heavy rainfall disturbances on a road network in California, the United States, abundant experiments and visualizations are conducted to demonstrate the rationality of our proposed comprehensive resilience indicators as well as the precision and reliability of these predictive resilience assessment outcom
Although deep face recognition has achieved impressive progress in recent years, controversy has arisen regarding discrimination based on skin tone, questioning their deployment into real-world scenarios. In this pape...
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5G technology is constrained by its higher frequency band and smaller coverage area, which leads to the need for operators to use technologies such as small cell base stations to increase the density of base station d...
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ISBN:
(纸本)9781665432078
5G technology is constrained by its higher frequency band and smaller coverage area, which leads to the need for operators to use technologies such as small cell base stations to increase the density of base station deployment to ensure signal coverage quality, which leads to more enormous construction costs. Therefore, it is urgent to find a safe and reliable solution that can use the existing public network to realize small cell base stations with automatic access. Based on this demand, we summarize various existing small cell base station automatic access technology solutions and their advantages and disadvantages while combing the characteristics of blockchain technology and its application solutions in similar scenarios. And then, we propose a new blockchain-based base station automatic access solution and implements the system in a practical scenario. In our solution, we innovatively introduce blockchain as an intermediate manager for small base stations and core networks, which solves traditional solutions requiring customized equipment while ensuring safety and reliability. It reduces costs and improves efficiency, but some problems are brought by the “decentralization” of blockchain waiting to be solved.
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