Search Results (41)

In semiconductor packaging and microelectronic manufacturing, inkjet printing technology is widely employed in critical processes such as conductive line fabrication and encapsulant dot deposition. However, dynamic printing defects, such as missing droplets and splashing can severely compromise circuit continuity and device reliability. Traditional inspection methods struggle to detect such subtle and low-contrast defects. To address this challenge, we propose MCHB-DETR, a novel lightweight defect detection framework based on RT-DETR, aimed at improving product yield in inkjet printing for semiconductor packaging. MCHB-DETR features a lightweight backbone with enhanced multi-level feature extraction capabilities and a hybrid encoder designed to improve cross-scale and multi-frequency feature fusion. Experimental results on our inkjet dataset show a 29.1% reduction in parameters and a 36.7% reduction in FLOPs, along with improvements of 3.1% in mAP@50 and 3.5% in mAP@50:95. These results demonstrate its superior detection performance while maintaining efficient inference, highlighting its strong potential for enhancing yield in semiconductor packaging. Full article

Recent advances in 3D printing of silica glass have highlighted the limitations of conventional stereolithography (SLA), which requires high-temperature sintering (≈1000 °C) and often uses slurry-based materials. To address these limitations, a sinterless approach using polyhedral oligomeric silsesquioxane (POSS)-based resin has gained attention, as it can form transparent fused silica at only 650 °C. However, previous POSS-based systems suffered from high shrinkage owing to the addition of organic monomers. In this study, a novel low-viscosity polymerizable POSS resin was synthesized without additional monomers, maintaining its sinterless properties while reducing shrinkage. Experimental results showed that our POSS resin has a silica content of 41%, with a shrinkage rate of only 36 ± 1%, which effectively reduced cracking and warping when calcinating large-volume models. It was demonstrated that this resin can be applied not only to high-resolution glass 3D printing with sub-200 nm line widths using two-photon polymerization, but also to low-cost glass 3D printing using single-photon polymerization. The 3D-printed objects can be converted into silica glass structures at significantly lower temperatures than traditional sintering, offering a promising route for efficient and precise glass manufacturing. Potential applications of our POSS resin include the production of multi-scale devices, such as microfluidic devices and optical components, and hybrid processing with semiconductors and MEMS and photonic devices. Full article

Gas sensors are considered a highly effective non-destructive technique for monitoring the quality and safety of food materials. These intelligent sensors can detect volatile profiles emitted by food products, providing valuable information on the changes occurring within the food. Gas sensors have garnered significant interest for their numerous advantages in the development of food safety monitoring systems. The adaptable characteristics of gas sensors make them ideal for integration into production lines, while the flexibility of certain sensor types allows for incorporation into packaging materials. Various types of gas sensors have been developed for their distinct properties and are utilized in a wide range of applications. Metal-oxide semiconductors and optical sensors are widely studied for their potential use as gas sensors in food quality assessments due to their ability to provide visual indicators to consumers. The advancement of new nanomaterials and their integration with advanced data acquisition techniques is expected to enhance the performance and utility of sensors in sustainable practices within the food supply chain. Full article

The traditional semiconductor packaging training process is time-consuming and carries the risk of damaging precision equipment due to improper operation. Additionally, the retirement of experienced trainers has led to loss of specialized training and testing expertise. To address these challenges, digital twin technology is applied to training packaging engineers. We conducted an empirical study at the packaging production line of the Minghsin University of Science and Technology to address talent training bottlenecks and imbalances between supply and demand. First, an integrated software and hardware system was designed by combining digital twin and mixed reality (MR). The development process of the digital twin system for the wafer-dicing machine includes on-site visits, machine operation instructions, certification content development, expert validity construction, small-scale testing and modifications. We compared the pre- and post-experiment scores of industry experts to evaluate the operation time of five participants and their feedback. Digital twin and MR for simulated training increased proficiency in operation. The digital twin training and certification model developed in this study improved students’ pass rates in certification exams. Full article

In recent years, the semiconductor industry has embraced advanced artificial intelligence (AI) techniques to facilitate intelligent manufacturing throughout their organizations, with particular emphasis on virtual metrology (VM) systems. Nonetheless, the practical application of data-driven virtual metrology for product quality inspection encounters notable hurdles, such as annotating inspections in highly dynamic industrial environments. This leads to complexities and significant expenses in data acquisition and VM model training. To address the challenges, we delved into transfer learning (TL). TL offers a valuable avenue for knowledge sharing and scaling AI models across various processes and factories. At the same time, research on transfer learning in VM systems remains limited. We propose a novel parameter transfer learning (PTL) architecture for VM systems and examine its application in industrial process automation. We implemented cross-factory and cross-recipe transfer learning to enhance VM performance and offer practical advice on adapting TL to meet individual needs and use cases. By leveraging extensive data from Seagate wafer factories, known for their large-scale and high-dimensional nature, we achieved significant PTL performance improvements across multiple performance metrics, with the true positive rate (TPR) increasing by 29% and false positive rate (FPR) decreasing by 43% in the cross-factory study. In contrast, in the cross-recipe study, TPR increased by 27.3% and FPR decreased by 6.5%. With our proposed PTL architecture and its performance achievements, insufficient data from the new manufacturing sites, new production lines and new products are addressed with shorter VM model training time and smaller computational power with strong final quality prediction confidence. Full article

Vast amounts of data are continuously collected through sensors fitted into various pieces of equipment and processes in semiconductor production lines. These integrated datasets often encompass tens of thousands of dimensions, making it challenging to identify complex relationships among data dimensions for diagnosing defects and achieving high yield rates. Parallel Coordinate Plots (PCPs) are effective for visually analyzing multi-dimensional data, but traditional axis reordering methods struggle with superhigh-dimensional datasets. To address these challenges, we propose SDPC, an interactive PCP-based visual analysis system specifically tailored to the unique requirements of semiconductor production lines. SDPC employs a server–client architecture that efficiently visualizes sensor data in real time by dynamically selecting dimensions and down-sampling data based on user interactions. This enables engineers to explore high-dimensional sensor data without noticeable delays, enhancing their ability to identify defects quickly. By integrating user-defined filter conditions and focusing on defect-relevant dimensions, SDPC enhances interpretability and accelerates root cause identification. An evaluation with semiconductor production engineers demonstrated SPDC’s ability to facilitate real-time exploratory analysis, boost operational efficiency, reduce visual analysis time by two-thirds for on-site engineers, and ultimately lead to more effective production processes. Full article

With the rapid development of the semiconductor industry, the demand for high-speed testing in the large-scale production of semiconductor devices and integrated circuit production lines continues to grow. As one of the key tools in semiconductor device performance testing and integrated circuit testing, source measure unit (SMU) plays a crucial role in high-precision transient response testing scenarios. In high-precision measurement scenarios, multiple measurements are often required and averaged to improve measurement accuracy, but this can slow down the measurement speed. This article proposes a measurement acceleration algorithm based on BA-Informer time series prediction to solve the problem of decreased measurement speed in high-precision measurement. On the one hand, this algorithm improves the encoder structure. Traditional time series prediction models may have limitations in handling long-term dependencies and trend extraction. BiRNN is an extended version of recurrent neural network (RNN), which consists of two directional RNN. One forward RNN processes data from the beginning to the end of the sequence, while the other reverse RNN processes data from the end to the beginning of the sequence. In the end, the outputs from both directions are merged at each time step. Compared to traditional one-way RNN, BiRNN can more effectively handle data with before and after dependencies. Based on its characteristics, this article integrates BiRNN into the encoder structure. This algorithm can simultaneously process input sequences from both positive and negative directions, effectively limiting the bidirectional contextual information of data and significantly enhancing the model’s ability to capture time series trends. In this paper, BiRNN is integrated into the encoder structure, and the algorithm can simultaneously process input sequences from both positive and negative directions, more effectively capturing the bidirectional contextual information of data and significantly enhancing the model’s ability to capture time series trends. This improvement enables the model to more accurately grasp the overall trend of data changes during prediction, thereby improving prediction accuracy. On the other hand, an attention discrete cosine transform (ADCT) module is introduced between the encoder and decoder to convert time-domain signals into frequency-domain representations. This not only reveals the spectral characteristics of the signal but also reduces data redundancy and improves the efficiency of subsequent processing by combining attention mechanisms. Finally, the algorithm performance is analyzed by analyzing the output characteristic curves of loads with different properties. The experiment shows that the prediction algorithm and the combination of measurement and prediction method proposed in this article save half of the measurement time by combining measurement and prediction while ensuring the same amount of data obtained, verifying the effectiveness of the proposed method. Full article

The manufacturing industry environment is rapidly evolving into smart manufacturing. It prioritizes digital innovations such as AI and digital transformation (DX) to increase productivity and create value through automation and intelligence. Vision systems for defect detection and quality control are being implemented across industries, including electronics, semiconductors, printing, metal, food, and packaging. Small and medium-sized manufacturing companies are increasingly demanding smart factory solutions for quality control to create added value and enhance competitiveness. In this paper, we design and develop a high-speed defect detection system based on a line-scan camera using deep learning. The camera is positioned for side-view imaging, allowing for detailed inspection of the component mounting and soldering quality on PCBs. To detect defects on PCBs, the system gathers extensive images of both flawless and defective products to train a deep learning model. An AI engine generated through this deep learning process is then applied to conduct defect inspections. The developed high-speed defect detection system was evaluated to have an accuracy of 99.5% in the experiment. This will be highly beneficial for precision quality management in small- and medium-sized enterprises Full article

This paper utilizes the digital integrated circuit testing model to compute the test yield curve of future wafers and explore the influence of test guardband (TGB) on quality and yield. With the passage of three years since the COVID-19 pandemic disrupted semiconductor production lines, the semiconductor manufacturing industry still faces chip shortages. Although initiatives such as the CHIPS and Science Act in the United States have helped stabilize chip supply chains, manufacturers still face inventory shortages and delayed deliveries. Moreover, the backwardness and inaccuracy of semiconductor test equipment have led to a decline in both test yield and wafer quality, resulting in reduced shipments. Therefore, to mitigate yield losses and enhance the test yield and shipment volume of semiconductor products, this paper proposes a diverse test method (DTM) to improve test outcomes through the alteration of the testing strategy and TGB adjustment. Furthermore, according to the wafer estimation table published in the IEEE International Roadmap for Devices and Systems (2023), the proposed DTM can effectively enhance the test yield of wafers and improve the testing capabilities of ATE testers (automatic test equipment). Consequently, suppliers can stabilize the chip supply chain and enhance their companies’ profits and reputation by improving chip test yield. Full article

The out-of-control action plan (OCAP) is crucial in the wafer probing process of semiconductor manufacturing as it systematically addresses and corrects deviations, ensuring the high quality and reliability of semiconductor devices. However, the traditional OCAP involves many redundant and complicated processes after failures occur on production lines, which can delay production and escalate costs. To overcome the traditional OCAP’s limitations, this paper proposes a novel OCAP aimed at enhancing the wafer probing process in semiconductor manufacturing. The proposed OCAP integrates proactive measures such as preventive maintenance and advanced monitoring technologies, which are tested and verified through a comprehensive experimental setup. Implementing the novel OCAP in a case company’s production line reduced machine downtime by over 24 h per week and increased wafer production by about 23 wafers per week. Additionally, probe test yield improved by an average of 1.1%, demonstrating the effectiveness of the proposed method. This paper not only explores the implementation of the novel OCAP but also compares it with the traditional OCAP, highlighting significant improvements in efficiency and production output. The results underscore the potential of advanced OCAP to enhance manufacturing processes by reducing dependency on human judgment, thus lowering the likelihood of errors and improving overall equipment effectiveness (OEE). Full article

In the semiconductor manufacturing industry, achieving high yields constitutes one of the pivotal factors for sustaining market competitiveness. When confronting the substantial volume of high-dimensional, non-linear, and imbalanced data generated during semiconductor manufacturing processes, it becomes imperative to transcend traditional approaches and incorporate machine learning methodologies. By employing non-linear classification models, one can achieve more real-time anomaly detection, subsequently facilitating a deeper analysis of the fundamental causes behind anomalies. Given the considerable dimensionality of production line data in semiconductor manufacturing, there arises a necessity for dimensionality reduction to mitigate noise and reduce computational costs within the data. Feature selection stands out as one of the primary methodologies for achieving data dimensionality reduction. Utilizing wrapper-based heuristics algorithms, although characterized by high time complexity, often yields favorable performance in specific cases. If further combined into hybrid methodologies, they can concurrently satisfy data quality and computational cost considerations. Accordingly, this study proposes a two-stage feature selection model. Initially, redundant features are eliminated using mutual information to reduce the feature space. Subsequently, a Simplified Swarm Optimization algorithm is employed to design a unique fitness function aimed at selecting the optimal feature subset from candidate features. Finally, support vector machines are utilized as the classification model for validation purposes. For practical cases, it is evident that the feature selection method proposed in this study achieves superior classification accuracy with fewer features in the context of wafer anomaly classification problems. Furthermore, its performance on public datasets further substantiates the effectiveness and generalization capability of the proposed approach. Full article

Lung cancer is the third most common cancer in women and the most common cancer in males. Chemotherapy, allopathy, hormone therapy, radiation therapy, surgery, immune system, and targeted therapies are frequently used to treat lung cancer. These medications induce other diseases and have a variety of negative effects. Thus, we used a different strategy and sought to treat lung cancer with medicinal herbs. We selected the perennial creeping herb Bacopa monnieri, which belongs to the Scrophulariaceae family, among other medicinal herbs. It contains several active phytoconstituents, including sterols, alkaloids, flavanoids, terpenoids, and saponins. The primary component with anti-lung cancer efficacy is phytosterol, according to the components. According to the phytochemical investigation, this plant contained it. The literature review indicates that the problem is lessened by nanoparticle production. Thus, the novelty of our work is the manufacture of zinc oxide nanoparticles for the treatment of lung cancer using BM stem extracts. Researchers have been interested in ZnO material because of its huge band gap (3.37 eV) with n-type semi-conductivity and high excitonic binding energy (60 meV) with regards to the different semiconductor nanomaterials, such as TiO₂, SnO₂, GaN, CuO, GaAs, Si, and ZnO. Zinc oxide in bulk is economical and can be used for many different industrial processes, such as the creation of nanoparticles. Zinc acetate serves as the precursor and stem extract serves as the reducing agent in the synthesis. The absorbance peak between 300 and 400 nm in UV spectroscopy was used to characterize the ZnO nanoparticles that were produced from hydromethanolic BM stem extract. In later research, lung cancer treatment might be considered. Given that lung (A549) cell lines will be treated with phytosterol-containing hydromethanolic BM stem extract in the form of ZnO nanoparticles, which will cause cell death by reducing cell proliferation, DNA damage and apoptosis may occur. Full article

In semiconductor manufacturing, defect inspection in non-patterned wafer production lines is essential to ensure high-quality integrated circuits. However, in actual production lines, achieving both high efficiency and high sensitivity at the same time is a significant challenge due to their mutual constraints. To achieve a reasonable trade-off between detection efficiency and sensitivity, this paper integrates the time delay integration (TDI) technology into dark-field microscopy. The TDI image sensor is utilized instead of a photomultiplier tube to realize multi-point simultaneous scanning. Experiments illustrate that the increase in the number of TDI stages and reduction in the column fixed pattern noise effectively improve the signal-to-noise ratio of particle defects without sacrificing the detecting efficiency. Full article

High-resolution imaging of Cu/low-k on-chip interconnect stacks in advanced microelectronic products is demonstrated using full-field transmission X-ray microscopy (TXM). The comparison of two lens-based laboratory X-ray microscopes that are operated at two different photon energies, 8.0 keV and 9.2 keV, shows a contrast enhancement for imaging of copper nanostructures embedded in insulating organosilicate glass of a factor of 5 if 9.2 keV photons are used. Photons with this energy (Ga-Kα radiation) are generated from a Ga-containing target of a laboratory X-ray source applying the liquid-metal-jet technology. The 5 times higher contrast compared to the use of Cu-Kα radiation (8.0 keV photon energy) from a rotating anode X-ray source is caused by the fact that the energy of the Ga-Kα emission line is slightly higher than that of the Cu-K absorption edge (9.0 keV photon energy). The use of Ga-Kα radiation is of particular advantage for imaging of copper interconnects with dimensions from several 100 nm down to several 10 nm in a Cu/SiO₂ or Cu/low-k backend-of-line stack. Physical failure analysis and reliability engineering in the semiconductor industry will benefit from high-contrast X-ray images of sub-μm copper structures in microchips. Full article

Magnetic levitation can reduce particulate contamination that occurs during wafer transportation in the semiconductor manufacturing process. This technology radically eliminates contact between the wafer and the transport system, reducing friction, wear, and particle generation. Therefore, it is suitable for achieving high cleanliness in the ultra-fine line-width semiconductor production process and solving the need for particle removal in a vacuum environment. In this study, the roller and linear motion guide components of the wafer transfer system were replaced with a magnetic levitation module, and a robot arm was installed on top to transport a single wafer. A posture controller and a current controller were designed, and test equipment simulating the wafer transfer system was also manufactured and tested. Regarding mover and system identification, a sine sweep test was performed on the motion axis of the five degrees of freedom. Through the obtained system identification, it was possible to design the posture controller more precisely. Moreover, through levitation in standstill experiments and high-speed operation experiments, the wafer transport system can be used to verify dust-free high-speed transport and accurate positioning performance. Full article