The application of fuzzy theory in stealth laser dicing technology for wafers aims to enhance the precision and efficiency of cutting parameters. With technological advancements and the growth of the electric vehicle industry, the demand for semiconductor chips has rapidly increased, driving wafer cutting technology towards greater efficiency and precision. Compared to traditional blade cutting and laser cutting techniques, stealth laser dicing technology can avoid the chipping caused by blade cuts and the thermal damage caused by laser cuts, providing a stress-free and thermal damage-free solution. This study optimizes cutting parameters through a fuzzy control system using a layered setting method. The first layer determines the most suitable laser power based on the thickness of the workpiece and the metal layer; the second layer uses the final wafer thickness and the laser power determined in the first layer as inputs to calculate the optimal scribing height. These parameter settings aim to enhance cutting quality and efficiency while also verifying the practical application effects of the fuzzy control system. Experimental results show that the fuzzy control system significantly improves cutting precision and efficiency, achieving a $100 \%$ cutting success rate, a ${6 6 \%}$ reduction in material consumption, a 4% reduction in energy usage, and a $50 \%$ reduction in parameter adjustment labor time. The precision of the fuzzy control system in controlling laser power surpasses manual settings, and the optimization of scribing height is also significantly improved. The study demonstrates that fuzzy theory has extensive potential applications in stealth laser dicing of wafers. Future research may integrate artificial intelligence technology to further enhance the automation of cutting parameter settings, thereby improving production efficiency and product quality.
Advancements in network technology, such as 5G and 6G systems, have increased the need for large-scale data communication. This has led to a higher demand for efficient FFT processors capable of handling big data. In this paper, we propose a low-power variable-length FFT architecture with distributed memory to enhance data processing efficiency. The architecture employs an auto-correction rotator to efficiently and accurately generate the corresponding twiddle factors without excessive memory storage. To further reduce power consumption, distributed memory is used for storing computational data, with dynamic pointers facilitating data access. Simulations demonstrate improvements in processing speed and memory efficiency, addressing the demands for efficient data transmission. The proposed FFT chip design shows promise for application in next-generation network systems.
Designing a quantum circuit that prepares userspecific states is a fundamental task in quantum computation. This involves loading user-provided data into the initial superposition state of a quantum register, enabling further processing by quantum algorithms. To address this state preparation problem, a quantum circuit utilizing rotation Y gates and controlled rotation Y gates has been previously proposed. However, the method for determining the rotation angles of these Y gates has not been detailed. This paper introduces a closed-form method for computing these angle parameters based on user-provided data. Numerical examples demonstrate the ease of use of the proposed method, which is implemented using IBM’s quantum information software kit (Qiskit). Additionally, real-world feature data from iris flowers is used to show that the feature data can be accurately loaded into the quantum register, facilitating tasks such as machine learning, dimensionality reduction, or pattern recognition through quantum computation.
This study uses Yolov5 object detection technology to detect the deterioration and defects of the painted layer of oil paintings. Two non-destructive detection light source image data, ultraviolet light and normal light will be used to identify refills, insect removal, and missing painted layers to assist Restoration engineers in speeding up the efficiency of missing annotation work in the work review process.This study applied the data augmentation method to increase the sample size and class balance. The results of this study show that the data augmentation method and class balancing can effectively improve the performance of the model; the performance of the proposed oil painting layer defect detection model has reached more than 98% in the class of insect exclusion and 70% in the class of retouching and painting layer loss. The study’s results can initially assist the restorer in the inspection process, effectively detect and locate defects, and provide the defect type and location to the restorer for reference, shortening the inspection process time and improving operational efficiency.
Given an unlabeled time series dataset, it is often required to cluster similar time series data. Dynamic Time Warping (DTW) is one of the useful methods to evaluate the similarity between time series data. However, the algorithm of DTW has a high computational cost and requires substantial computation time. To solve the issue, a DTW distance calculator has been proposed to reduce the computation time of DTW distance calculations. Additionally, a conventional circuit using neuron CMOS Winner-Take-All (vCMOS WTA) circuit has also been proposed for the minimum value determination circuit used in the DTW calculator. However, the proposed minimum value determination circuit was still unstable. In this paper, we propose a new minimum value determination circuit for use in the DTW distance calculator. In addition, this paper describes the circuit design, the circuit operation, and the results of HSPICE simulations.
