Semiconductor Equipment Research Articles

반도체 장비 교육을 위한 PLC기반 PID제어 가상 시스템 개발 및 구현에 관한 연구

IIn line with the government’s recent semiconductor talent training policy, vocational high schools and universities are receiving substantial funding for facility and equipment construction. Building semiconductor cleanroom facilities and processing equipment is very costly, while the limited lab space makes providing semiconductor education difficult. Therefore, an alternative education method which solves these issues is needed. To make PID control learning of semiconductor equipment more accessible in terms of cost, space, and difficulty, this study implemented a virtual system in a virtual simulation space, and confirmed the educational effectiveness through a comparative analysis of the real situation and virtual simulation.

Comparative Analysis of Financial Ratios of Selected Semiconductor Equipment Firms During 2020-2023

As a matter of fact, the semiconductor equipment industry has been developing rapidly. On this basis, this research provides a comparative analysis of the financial ratios of four selected semiconductor equipment firms, i.e., Axcelis Technologies (ACLS), ACM Research (ACMR), Onto Innovation (ONTO), and Veeco Instruments (VECO), over the period from 2020 to 2023. With this in mind, the study aims to evaluate the financial health, operational efficiency, as well as market performance of these firms in order to identify the optimal investment target for value investing. According to the analysis, a specific investing target is selected. Overall, this research is useful in comparing assets and liabilities, gross revenue, stocks, bonds, return on investment, and other specifications of each of these companies that bring out their strong and weak areas. Based on the analysis, ACLS emerges as the most promising candidate due to its robust financial performance and stable market valuation.

Analyzing the Trade Pattern of the Korea’s Semiconductor Equipment, its Parts and Components Industry

Analyzing the Trade Pattern of the Korea’s Semiconductor Equipment, its Parts and Components Industry

A Study on Improving the Heat Transfer Path of Semiconductor Equipment Using TRIZ

A Study on Improving the Heat Transfer Path of Semiconductor Equipment Using TRIZ

An effective strategy for manufacturing ultra-high purity AISI 316L stainless steel by vacuum C deoxidization pretreatment plus Mg–Ca composite treatment

Aiming at the special ultra-high purity (UHP) requirements of AISI 316L stainless steel used in semiconductor equipment, a strategy of vacuum C deoxidization pretreatment plus Mg–Ca composite treatment was proposed in this work. The effects of this strategy on cleanness and inclusion modification were systematically investigated by experimental observations and thermodynamic calculations. The results demonstrated that this strategy significantly reduced the O and S contents to 0.0005 wt% and 0.0007 wt%, respectively. The main inclusions after vacuum C deoxidization were large-sized Al2O3·SiO2 and MnS. After Mg treatment, the oxide inclusions were gradually modified into MgO·Al2O3 and MgO, while the sulfide inclusion was still MnS. After additional Ca treatment, the inclusions were further modified into finer CaO, MgO, CaO·MgO, and CaS. Moreover, this strategy remarkably decreased the number and size of the inclusions. The best level was that all inclusions were smaller than 2 μm, and the proportion of inclusions smaller than 1 μm was as high as 75.47%. Meanwhile, the evolution mechanism of inclusions during different treatments was clarified in this work. Finally, the optimum process for preparing UHP AISI 316L stainless steel was proposed as vacuum C deoxidation plus 0.002 wt% Mg and 0.0016 wt% Ca composite treatment.

A Machine-Learning Strategy to Detect Mura Defects in a Low-Contrast Image by Piecewise Gamma Correction.

A detection and classification machine-learning model to inspect Thin Film Transistor Liquid Crystal Display (TFT-LCD) Mura is proposed in this study. To improve the capability of the machine-learning model to inspect panels' low-contrast grayscale images, piecewise gamma correction and a Selective Search algorithm are applied to detect and optimize the feature regions based on the Semiconductor Equipment and Materials International Mura (SEMU) specifications. In this process, matching the segment proportions to gamma values of piecewise gamma is a task that involves derivative-free optimization which is trained by adaptive particle swarm optimization. The detection accuracy rate (DAR) is approximately 93.75%. An enhanced convolutional neural network model is then applied to classify the Mura type through using the Taguchi experimental design method that identifies the optimal combination of the convolution kernel and the maximum pooling kernel sizes. A remarkable defect classification accuracy rate (CAR) of approximately 96.67% is ultimately achieved. The entire defect detection and classification process can be completed in about 3 milliseconds.

Pruning Quantized Unsupervised Meta-Learning DegradingNet Solution for Industrial Equipment and Semiconductor Process Anomaly Detection and Prediction

Machine- and deep-learning methods are used for industrial applications in prognostics and health management (PHM) for semiconductor processing and equipment anomaly detection to achieve proactive equipment maintenance and prevent process interruptions or equipment downtime. This study proposes a Pruning Quantized Unsupervised Meta-learning DegradingNet Solution (PQUM-DNS) for the fast training and retraining of new equipment or processes with limited data for anomaly detection and the prediction of various equipment and process conditions. This study utilizes real data from a factory chiller host motor, the Paderborn current and vibration open dataset, and the SECOM semiconductor open dataset to conduct experimental simulations, calculate the average value, and obtain the results. Compared to conventional deep autoencoders, PQUM-DNS reduces the average data volume required for rapid training and retraining by about 75% with similar AUC. The average RMSE of the predictive degradation degree is 0.037 for Holt–Winters, and the model size is reduced by about 60% through pruning and quantization which can be realized by edge devices, such as Raspberry Pi. This makes the proposed PQUM-DNS very suitable for intelligent equipment management and maintenance in industrial applications.

Position-Dependent Motion Feedforward via Gaussian Processes: Applied to Snap and Force Ripple in Semiconductor Equipment

Position-Dependent Motion Feedforward via Gaussian Processes: Applied to Snap and Force Ripple in Semiconductor Equipment

High-precise Motion Control Hardware System and Key Timing Management Research

High-precise and multi-axes motion control system is required in the application of semiconductor equipment and high-performance industrial equipment. To achieve the tight performance requirements, this paper presents the control system architecture, communication, and timing management solution. We use multiple cores DSP and FPGA as the main control chips, use SRIO @5Gbps *4 as the DSP and FPGA communication protocol, and use Aurora 8B/10B @5Gpbs as the inter-board communication protocol. This architecture with its high sampling rate and low latency can improve the motion control performance and raise the system sampling bandwidth. Analysis and test results of system communication and timing management are included to demonstrate the efficacy of the proposed scheme.

Developing a Monitoring System for Wire Bonders Using Semiconductor Equipment and Materials International (SEMI) Equipment Communications Standard Sensors

Developing a Monitoring System for Wire Bonders Using Semiconductor Equipment and Materials International (SEMI) Equipment Communications Standard Sensors

Improved impedance matching speed with gradient descent for advanced RF plasma system

Plasma processes are widely used in the fabrication of integrated circuits for the smaller, faster, more powerful circuits demanded by new technology. High-performance semiconductor equipment is required for dynamic random-access memory and three-dimensional NAND flash memory in semiconductor manufacturing. To enhance the speed of RF impedance matching, this paper proposes a method for calculating the load impedance of the plasma chamber. To calculate the target value of the variable components, we employ the input impedance of the impedance-matching unit and the complex impedances of the variable components. In addition, the internal parameters are automatically tuned through the gradient-descent method of machine learning, which improves the accuracy of impedance matching. The new matching method achieved a fast matching time and high efficiency of the matching trajectories. Moreover, it avoids the increase of the reflected power during the matching process, which causes plasma instability, and prohibits significant matching delay in a specific area, which disadvantages the conventional matching algorithm. These advantages are expected to significantly expand the development of new plasma processes from that of conventional impedance matching.

A MEMS Thermopile Pirani Sensor Integrated With Composite Nanoforests for Vacuum Monitoring in Semiconductor Equipment

A MEMS Thermopile Pirani Sensor Integrated With Composite Nanoforests for Vacuum Monitoring in Semiconductor Equipment

Integration of Artificial Intelligence for Real-Time Fault Detection in Semiconductor Packaging

Semiconductor manufacturing involves a complex sequence of unit processes, where even a minor error can disrupt the entire production chain. Present-day manufacturing setups rely on continuous data monitoring of equipment health, wafer measurements, and inspections to identify any abnormalities that could impact the quality and performance of the final chip. The primary aim is fault detection and classification (FDC), for which a range of techniques including statistical analysis and machine learning algorithms are commonly employed. In this study, we introduce an innovative approach utilizing an artificial immune system (AIS), inspired by biological mechanisms, for FDC in semiconductor equipment. The main culprits behind process failures are shifts caused by the aging of parts and modules over time. Our methodology integrates state variable identification (SVID) data, reflecting current equipment conditions, and optical emission spectroscopy (OES) data, capturing plasma process information under faulty scenarios induced by deliberate gas flow rate adjustments in semiconductor fabrication. Our results demonstrate a modeling prediction accuracy of 94.69% when incorporating selected SVID and OES data, and 93.68% accuracy using OES data alone. In conclusion, we suggest the potential application of AIS in semiconductor process decision-making, offering promising avenues for enhancing fault detection in semiconductor equipment.

반도체 장비 기업의 유형별 혁신 속성 및 기술지식 영향력 요인 연구

반도체 장비 기업의 유형별 혁신 속성 및 기술지식 영향력 요인 연구

Analysis of the global trade network of the chip industry chain: Does the U.S.-China tech war matter?

Chip is the “brain” of the information industry and modern manufacturing industry, and supply chain security is the key to the sustainable development of the industrial chain. From the perspective of the industrial chain, this paper selected semiconductor silicon wafers and equipment, integrated circuits, electronic computers, and components as representative commodities in the upstream, midstream, and downstream of the chip industry chain, constructed global trade networks of the chip industry chain, and analyzed the characteristics of the networks and nodes in 2020 and the changes in China's status before and after the China-U.S. tech war. The study results indicate that the network scale and network density of the global trade network of downstream electronic computers and components are higher than those of midstream integrated circuits; the global trade network of upstream semiconductor wafers and equipment has the smallest network scale and network density, and the trade networks of all links show obvious small-world characteristics; The United States ranks first in betweenness centrality of all links, with the strongest control ability and the largest number of trading partners in all links; China has higher betweenness centrality and more trading partners in the global trade network of the two upstream commodities than that of the midstream commodities, and the lowest betweenness centrality in the global trade network of the downstream commodities; The core countries of the chip industry chain are concentrated in southeast Asia, east Asia, central and western Europe, and the United States. China's trade status of semiconductor silicon wafers and integrated circuits has declined significantly during the China-U.S. tech war. The nodes in the chip trade network have good robustness in the face of random attacks and show vulnerability under target attacks. Additionally, the trade network's robustness in the chip industry chain is the strongest for downstream commodities, ranks second for midstream commodities, and is the weakest for upstream commodities. These findings can provide references for ensuring chip supply chain security in China and other trade-participating countries.

CPU company investment advice

The semiconductor industry is the backbone of the information technology sector. It is a strategic, fundamental, and leading sector that supports economic and social development and ensures national security, and its technical level and development scale have become one of the key indicators of a nation's overall national strength and industrial competitiveness. In 2019 Chinese mainland semiconductor equipment sales will be 13.45 billion US dollars. It is crucial to evaluate the stock in the semiconductor industry, which would help the investor to investment. This paper primarily offers a thorough examination of the semiconductor business from a data perspective. The article selected AMD and QCOM.AMD and intel corporation to analyze the investment value based on profit analysis, market analysis, and risk analysis. Based on a variety of analysis frameworks, the article determines that QCOM is the most strongly advised investment. Nonetheless, QCOM's unusual data still implies that there is a significant risk involved in investing in QCOM. Investors may make enormous profits from it as well.

Review on Wearable Sensors of the Medical System

A framework known as "smart healthcare" makes use of technologies like wearable's, and the Internet of Medical Things (IoT), In order to intelligently manage and respond to the needs of the health environment, we need to combine data, machine learning algorithms, wireless communication technology, and the Internet of Things (IoT) to link people, resources, and organizations; make health records easily accessible; and share the data widely. Medical sensors, or IoT, are one of the key components of smart healthcare. Due to the complexity of illnesses, Disease diagnosis often necessitates the use of multiple types of medical signals. The most crucial concern when employing multimodal signals is how to fuse them, which is a topic of growing interest among researchers. The newest generation of personal portable gadgets for telemedicine practice is wearable health monitoring systems. However, due to the inapplicability of the building process used for standard semiconductor equipment, wearable healthcare equipment research and commercialization are currently progressing at a very sluggish rate. Despite these obstacles, developments in materials flag, chemical analysis techniques, apparatus, and manufacturing processes created a groundwork for whole new wearable technology, which has continued to evolve. &nbsp

A Machine Learning Approach for Improving Wafer Acceptance Testing Based on an Analysis of Station and Equipment Combinations

Semiconductor manufacturing is a complex and lengthy process. Even with their expertise and experience, engineers often cannot quickly identify anomalies in an extensive database. Most research into equipment combinations has focused on the manufacturing process’s efficiency, quality, and cost issues. There has been little consideration of the relationship between semiconductor station and equipment combinations and throughput. In this study, a machine learning approach that allows for the integration of control charts, clustering, and association rules were developed. This approach was used to identify equipment combinations that may harm production processes by analyzing the effect on Vt parameters of the equipment combinations used in wafer acceptance testing (WAT). The results showed that when the support is between 70% and 80% and the confidence level is 85%, it is possible to quickly select the specific combinations of 13 production stations that significantly impact the Vt values of all 39 production stations. Stations 046000 (EH308), 049200 (DW005), 049050 (DI303), and 060000 (DC393) were found to have the most abnormal equipment combinations. The results of this research will aid the detection of equipment errors during semiconductor manufacturing and assist the optimization of production scheduling.

미･중 갈등 이후 중국 반도체 장비의 공급망 구조 변화

반도체가 경제안보 시대에 가장 중요한 국가전략기술로 부상하면서, 우리나라도 반도체 산업에 대한 중요성과 공급망 내재화, 경쟁력 강화 전략 등을 추진하고 있다. 게다가, 미국 은 대만, 우리나라 파운드리 기업들의 투자유치를 확대하면서 우방국 중심의 협력체계를 강화해 가고 있다. 이처럼 급변하는 글로벌 반도체 공급망 재편 속에서 우리나라는 변화를 면밀히 고찰하고, 이에 대한 대응방안을 준비해야 하는 중요한 시점에 있다. 따라서, 본 논문에서는 미중 갈등이후 중국의 반도체 공급망 구조가 어떻게 변하고 있는 지, 미중 관계를 둘러싼 글로벌 반도체 공급망 재편이 어떻게 전개되고 있는지 반도체 장비 산업을 중심으로 살펴보았다. 미중 갈등이후, 양국 간의 교역 네트워크를 분석한 결과, 오히 려 양국간의 교역은 더욱 활성화 되었으며, 세계 최대 반도체 수요시장인 중국을 중심으로 글로벌 교역이 오히려 활발하게 진행되고 있다. 즉, 양국 간의 교역에서는 뚜렷한 블록화가 나타나고 있지 않았다. 다만, 더욱더 강력해지는 미국의 대중국 반도체 수출통제로 중국은 반도체 장비의 국산화율을 제고되고 있다. 미국의 제재가 중국 반도체 장비 업체의 기술개 발을 더 촉진하면서 인력과 자금투입이 확대되 국산화율이 높아지고 있다. 즉, 미국의 제개 가 도리어 중국 반도체 장비의 국산화를 촉진시키는 촉매제가 되고 있다. 우리 반도체 산업은 미국이 반도체 분야에 전반적인 공급망을 내재화하고, 중국이 지속 적인 연구개발 및 투자로 기술추격을 가속화 할 경우를 대비해야 한다. 중국에 생산거점을 지속적으로 활용하면서도, 시장수요도 확대하고 활용할 수 있는 전략에 대한 고민이 필요하며, 장기적 관점에서 중국과도 협력할 수 있는 방안을 고민해야 할 것이다.

Simulation, Analysis, and Experimentation of the Compliant Finger as a Part of Hand-Compliant Mechanism Development

Compliant mechanisms are gaining popularity in many different fields, such as in microelectromechanical systems (MEMS), medical applications and health care, opto-mechatronic technology, aerospace engineering, and semiconductor equipment. One of the areas for utilizing compliant mechanisms is building models of human hand counterparts. These models are often used as grasping and rehabilitation devices. Because of their properties, creating a human hand counterpart with compliant mechanisms is a much better choice compared with the models with traditional mechanisms; it looks more realistic, and its movements are much more natural compared with models with a traditional mechanism. A method of modeling and designing such a bio-inspired mechanism, as well as its experimental analysis with various forces applied, is presented in this paper. Two prototypes of the compliant fingers were obtained by 3D printing, and the calculation of the bending angle values was achieved by applying image processing to camera images of the compliant fingers’ prototypes. Image processing was conducted on images taken for both loaded and unloaded 3D-printed compliant finger prototype positions. Finally, these bending angle results are compared with the results obtained by Finite Element Method (FEM) analysis and experimental results acquired by a digital protractor.