Semiconductor Equipment Research Articles

Temperature Uniformity Control of 12-Inch Semiconductor Wafer Chuck Using Double-Wall TPMS in Additive Manufacturing.

In semiconductor inspection equipment, a chuck used to hold a wafer is equipped with a cooling or heating system for temperature uniformity across the surface of the wafer. Surface temperature uniformity is important for increasing semiconductor inspection speed. Triply periodic minimal surfaces (TPMSs) are proposed to enhance temperature uniformity. TPMSs are a topic of increasing research in the field of additive manufacturing and are a type of metamaterial inspired by nature. TPMSs are periodic surfaces with no intersections. Their continuous curve offers self-support during the additive manufacturing process. This structure enables the division of a single space into two domains. As a result, the heat transfer area per unit volume is larger than that of general lattice structures, leading to a superior heat transfer performance. This paper proposes a new structure called a double-wall TPMS. The process of creating a double-walled TPMS by adjusting the thickness of the sheet TPMS was investigated, and its thermal performance was studied. Finally, a double-wall TPMS was applied to the chuck. The optimal designs for the diamond and gyroid structures exhibited a difference in surface temperature uniformity of 0.23 °C and 0.66 °C, respectively. Accordingly, the models optimized with the double-wall TPMS are proposed.

Analysis of the structure and robustness of the global semiconductor trade network.

Amidst the global restructuring of the semiconductor supply chain, this paper constructs a global semiconductor trade network (2007, 2012, 2017, 2021) encompassing three segments (raw materials, equipment, and finished components), based on the CEPII database. After initially exploring trade flows among different regions, the paper conducts an in-depth analysis of the network's overall structure and the significance of its nodes. Furthermore, the evolution of the trade network's community structure is discussed and its robustness and dynamics over recent years are assessed through computer program simulation. The findings are as follows: First, semiconductor trade flows are concentrated primarily among a few regions in Asia, US, and EU. Second, the network has grown in size and exhibits significant "small-world" characteristics in all segments, deviating from the typical "sparsity" seen in large-scale networks. Third, Japan, the US, and a few European regions wield significant influence in semiconductor materials and equipment trade, while Asian economies such as Chinese mainland, Chinese Taiwan, and Korea dominate semiconductor components trade. Fourth, the raw materials trade network has diversified in recent years, while the trade networks for equipment and finished components remain in a state of continuous "polarization." Fifth, the semiconductor trade network demonstrates robustness against random attacks but collapses quickly under targeted attacks. Among the three segments, the trade network of finished components, being larger in scale, exhibits greater resilience against both random and targeted attacks. This paper not only enhances the construction of the global semiconductor trade network but also introduces a dynamic perspective, offering deeper insights into its structure and robustness. The insights gained from this analysis provide valuable guidance for policymakers and companies, especially amidst rapid technological change and geopolitical tensions.

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

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.

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

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

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.

Emerging Process and Assembly Challenges in Electronics Manufacturing

Semiconductor and Semi equipment industries expect to see a strong upturn in the next few years. 5G, AI, Edge Computing, Persistent Memory, Integrated Power Management, and the transition to sub 5nm silicon technology are all driving the need for innovative packaging solutions. These solutions integrate silicon produced with disparate process nodes and deliver maximum performance at optimal cost. Heterogeneous Integration, utilizing a multitude of interconnect methodologies (from Fan-out to Silicon Interposer, to Chiplet), addresses this challenge but requires unique solutions for efficient, cost effective die placement. This presentation will look at the challenges and potential approaches for efficient and cost effective solutions.

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.

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.