A golden template self-generating method for patterned wafer inspection

This paper presents a novel golden template self-generating technique for detecting possible defects in periodic two-dimensional wafer images. A golden template of the patterned wafer image under inspection can be obtained from the wafer image itself and no other prior knowledge is needed. It is a bridge between the existing self-reference methods and image-to-image reference methods. Spectral estimation is used in the first step to derive the periods of repeating patterns in both directions. Then a building block representing the structure of the patterns is extracted using interpolation to obtain sub-pixel resolution. After that, a new defect-free golden template is built based on the extracted building block. Finally, a pixel-to-pixel comparison is all we need to find out possible defects. A comparison between the results of the proposed method and those of the previously published methods is presented

A golden block based self-refining scheme for repetitive patterned wafer inspections

This paper presents a novel technique for detecting possible defects in two-dimensional wafer images with repetitive patterns using prior knowledge. It has a learning ability that is able to create a golden block database from the wafer image itself, modify and refine its content when used in further inspections. The extracted building block is stored as a golden block for the detected pattern. When new wafer images with the same periodical pattern arrives, we do not have to re-calculate its periods and building block. A new building block can be derived directly from the existing golden block after eliminating alignment differences. If the newly derived building block has better quality than the stored golden block, then the golden block is replaced with the new building block. With the proposed algorithm, our implementation shows that a significant amount of processing time is saved. And the storage overhead of golden templates is also reduced significantly by storing golden blocks only

Influence maximization in multilayer networks based on adaptive coupling degree

Influence Maximization(IM) aims to identify highly influential nodes to maximize influence spread in a network. Previous research on the IM problem has mainly concentrated on single-layer networks, disregarding the comprehension of the coupling structure that is inherent in multilayer networks. To solve the IM problem in multilayer networks, we first propose an independent cascade model (MIC) in a multilayer network where propagation occurs simultaneously across different layers. Consequently, a heuristic algorithm, i.e., Adaptive Coupling Degree (ACD), which selects seed nodes with high spread influence and a low degree of overlap of influence, is proposed to identify seed nodes for IM in a multilayer network. By conducting experiments based on MIC, we have demonstrated that our proposed method is superior to the baselines in terms of influence spread and time cost in 6 synthetic and 4 real-world multilayer networks

Quantum Communication Network Utilizing Quadripartite Entangled States of Optical Field

We propose two types of quantum dense coding communication networks with optical continuous variables, in which a quadripartite entangled state of the optical field with totally three-party correlations of quadrature amplitudes is utilized. In the networks, the exchange of information between any two participants can be manipulated by one or two of the remaining participants. The channel capacities for a variety of communication protocols are numerically calculated. Due to the fact that the quadripartite entangled states applied in the communication systems have been successfully prepared already in the laboratory, the proposed schemes are experimentally accessible at present

A collision-tolerant based anti-collision algorithm for large scale RFID system

Tag identification is an important issue in RFID system. Most existing anti-collision algorithms solely focus on reducing collision probability while suffering from vast idle slots. This paper proposes a collision-tolerant dynamic framed slotted Aloha (CE-DFSA) algorithm which attempts to identify multiple tags in a slot to reduce the total identification time in the process of identification. In CE-DFSA, tags are allocated with orthogonal Walsh Sequence (WS) so that multiple tags can be identified in a time slot without spreading the spectrum. Simulation results show that the proposed algorithm considerably accelerates the tag identification process with improved efficiency compared with existing anti-collision algorithms

Cascaded Entanglement Enhancement

We present a cascaded system consisting of three non-degenerate optical parametric amplifiers (NOPAs) for the generation and the enhancement of quantum entanglement of continuous variables. The entanglement of optical fields produced by the first NOPA is successively enhanced by the second and the third NOPAs from -5.3 $dB$ to -8.1 $dB$ below the quantum noise limit. The dependence of the enhanced entanglement on the physical parameters of the NOPAs and the reachable entanglement limitation for a given cascaded NOPA system are calculated. The calculation results are in good agreement with the experimental measurements.Comment: 5 pages, 4 figure