As the technology node develops toward its physical limit, lithographic hotspot detection has become increasingly important and ever-challenging in the computer-aided design (CAD) flow. In recent years, convolutional neural networks (CNNs) have achieved great success in hotspot detection. However, the interpretability of their hotspot prediction has yet to be considered. Compared with conventional lithography simulation and pattern matching-based methods, the black-box nature of CNNs wavers their practical applications with confidence. In this article, we propose the first interpretable CNN-based hotspot detector capable of providing high-detection accuracy and reliable explanations for hotspot identification. Specifically, we augment the training dataset with expanded error markers obtained and preprocessed from lithography simulation, which are then learned by an encoder-decoder architecture as intermediate features. We additionally introduce coordinate attention in the encoder to facilitate better-feature extraction. By learning these error markers and part of their surrounding metals as root cause hotspot features, our architecture achieves the highest-hotspot accuracy of 99.78% and the lowest-false positive rate of 5.29% compared to all prior work. Moreover, our method demonstrates the best visual and quantitative interpretability results when applying CNN interpretation methods.

The value of demand response (DR) for improving the operational efficiency of smart grids has been widely recognized. To further explore the potential DR flexibility, this article presents a novel framework for DR resource trading between multiple service providers (SPs) and end customers, in which SPs are supposed to offer diversified incentives to a resource trading center (RTC) while anticipating responses from the demand side. Distinguished from previous studies, in this work each end customer is endowed the authority to simultaneously trade with multiple SPs with the intention of maximizing their net utilities. The trading process between SPs and customers is demonstrated by a multileader multifollower Stackelberg game, due to its superiority of modeling problems with hierarchical decision-making behaviors. To solve the formulated game, an exact potential function is proposed to help determine the unique Nash equilibrium (NE) among SPs, which serves as the cornerstone for further deriving the Stackelberg equilibrium (SE), i.e., the ultimate outcome of the resource trading process. Simulation results verified that the DR flexibility was significantly increased compared with the case when there is only one SP responsible for the entire trading on behalf of all the end customers. Moreover, the proposed approach is also able to bring extra benefits for both SPs and customers in terms of net profit and net utility value.

Diverse customer demands are increasing the need for improved flexibility, adaptability, and transparency of production processes. This poses new challenges to modern manufacturing systems. Fortunately, advances associated with the fourth industrial revolution have provided many solutions to these challenges. In this study, an AutomationML (AML) and OPC UA-based solution is proposed for manufacturing process. By integrating AML and OPC UA, we developed a four-layer architecture dedicated to a manufacturing system referencing the Reference Architecture Model for Industry 4.0 (RAMI 4.0). Additionally, an experimental system was constructed to demonstrate the availability and applicability of the proposed approach. The system execution processes was also elaborated to better understand the processing of information flows across the whole system.

There are two basic concerns for supporting multi-dimensional range query in P2P overlay networks. The first is to preserve data locality in the process of data space partitioning, and the second is the maintenance of data locality among data items with an exponentially expanding rate and an exponentially extending rate. The first problem has been well addressed by using recursive decomposition schemes, such as Quad tree, K-d tree, Z-order, and Hilbert curve. While the second problem was recently identified by our novel data structure: HD Tree. This paper is a follow-up to our previous work in HD Tree. In this paper, we explore how data locality can be easily maintained, and how range query can be efficiently supported in HD Tree. This is done by introducing two basic routing strategies, hierarchical routing and distributed routing. Although hierarchical routing can be applied to any two nodes in the system, it generates high volume traffic towards nodes near the root, and has very limited options to cope with a node failure. On the other hand, distributed routing concerns source and destination pairs only at the same depth, but traffic load is bound to some nodes at two neighboring depths, and multiple options can be found to redirect a routing request. Because HD Tree supports multiple routes between any two nodes in the system, routing in HD Tree is very flexible, and can be designed for many purposes, like fault tolerance, or dynamic load balancing. Distributed Routing Oriented Combined Routing algorithm is one such routing strategies implemented so far. It is a hybrid algorithm combining advantages from both the hierarchical routing and the distributed routing. The experimental results show that the DROCR algorithm achieves considerable performance gain over the equivalent tree routing at the highest depth examined. In the experiment of supporting multi-dimensional range query, we employ the Z-order space filling curve over the HD Tree overlay layer. We are expecting that the performance of range query will vary proportionally with the change of range size, and reasonably with the increase of dimensionality.