• Journal of the Semiconductor & Display Technology
• /
• v.17 no.1
• /
• pp.62-65
• /
• 2018

In the era of the 4th Industrial Revolution, IoT products of various and specialized fields are being developed and produced. Especially, the generation of the artificial intelligence, robotic technology Multilayer substrates and packaging technologies in the notebook, mobile device, display and semiconductor component industries are demanding the need for flexible materials along with miniaturization and thinning. To do this, this work use FCCL (Flexible Copper Clad Laminate), which is a flexible printed circuit board (PCB), to implement FPCB (Flexible PCB), COF (Chip on Film) Use is known to be essential. In this paper, I propose a transfer device which prevents the occurrence of scratches by analyzing the mechanism of wrinkle and scratch mechanism during the transfer process of thin film material in which the thickness increases while continuously moving in air or solution.

• International journal of advanced smart convergence
• /
• v.13 no.2
• /
• pp.25-34
• /
• 2024

The paper investigates how the semiconductor and electric vehicle industries are addressing safety and security concerns in the era of autonomous driving, emphasizing the prioritization of safety over security for market competitiveness. Collaboration between these sectors is deemed essential for maintaining competitiveness and value. The research suggests solutions such as advanced autonomous driving technologies and enhanced battery safety measures, with the integration of AI chips playing a pivotal role. However, challenges persist, including the limitations of big data and potential errors in semiconductor-related issues. Legacy automotive manufacturers are transitioning towards software-driven cars, leveraging artificial intelligence to mitigate risks associated with safety and security. Conflicting safety expectations and security concerns can lead to accidents, underscoring the continuous need for safety improvements. We analyzed the expansion of electric vehicles as a means to enhance safety within a framework of converging security concerns, with AI chips being instrumental in this process. Ultimately, the paper advocates for informed safety and security decisions to drive technological advancements in electric vehicles, ensuring significant strides in safety innovation.

• Electronics and Telecommunications Trends
• /
• v.39 no.3
• /
• pp.1-12
• /
• 2024

As the demand for high-performance semiconductors, such as chips for artificial intelligence and high-bandwidth memory devices, increases along with the limitations of ultrafine processing technology in the semiconductor in-line process, advanced packaging becomes an increasingly important breakthrough technology for further improving semiconductor performance. Major countries, including Korea, the United States, Taiwan, and China, and large companies are strengthening their technological industry capabilities through the development of advanced packaging technology and policy support. Nevertheless, Korea has a lower level of development of related technologies by approximately 66% compared with the most advanced countries. Therefore, we aim to discover the needs for an advanced packaging research and development (R&D) policy through written expert interviews and importance satisfaction analysis. As a result, various implications for R&D policy directions are suggested to strengthen the technological capabilities and R&D ecosystem of the Korean advanced packaging technology.

• Journal of the Semiconductor & Display Technology
• /
• v.18 no.4
• /
• pp.40-45
• /
• 2019

The smart factory has developed with the help of several technologies such as automation, artificial intelligence, big data, smart sensors and communication protocols. The Internet of things(IOT) among communication protocols has become the key factor for the seamless integration of various manufacturing equipment. Therefore, it is important that the IOT cooperate with the standards of communication protocols proposed by the SEMI in the semiconductor industry. In this paper, we suggest a novel reference model of the communication protocols for semiconductor equipment by introducing an IOT service layer. With the IOT service layer, we can use the functions and the additional services provided by the IOT standards that give the inter-operability between factory machines and host computers. We implement the standard of the communication protocols for semiconductor equipment with the IOT service layer by using ns3 simulator. It concludes that it is necessary to provide the platform for the IOT service layer to deploy efficiently the proposed reference model of the communication protocols.

• Journal of the Semiconductor & Display Technology
• /
• v.22 no.4
• /
• pp.108-112
• /
• 2023

Precision manufacturing technology is rapidly developing due to the extreme miniaturization of semiconductor processes to comply with Moore's Law. Accurate and precise alignment, which is one of the key elements of the semiconductor pre-process and post-process, is very important in the semiconductor process. The center detection of wafer align marks plays a key role in improving yield by reducing defects and research on accurate detection methods for this is necessary. Methods for accurate alignment using traditional image sensors can cause problems due to changes in image brightness and noise. To solve this problem, engineers must go directly into the line and perform maintenance work. This paper emphasizes that the development of AI technology can provide innovative solutions in the semiconductor process as high-resolution image and image processing technology also develops. This study proposes a new wafer center detection method through variable thresholding. And this study introduces a method for detecting the center that is less sensitive to the brightness of LEDs by utilizing a high-performance object detection model such as YOLOv8 without relying on existing algorithms. Through this, we aim to enable precise wafer focus detection using artificial intelligence.

• Annual Conference of KIPS
• /
• 2021.11a
• /
• pp.924-926
• /
• 2021

AI (Artificial Intelligence) is being utilized in various fields and services to give convenience to human life. Unfortunately, there are many security vulnerabilities in today's ML (Machine Learning) systems, causing various privacy concerns as some AI models need individuals' private data to train them. Such concerns lead to the interest in ML systems which can preserve the privacy of individuals' data. This paper introduces the latest research on various attacks that infringe data privacy and the corresponding defense techniques.

• Journal of the Semiconductor & Display Technology
• /
• v.23 no.2
• /
• pp.129-134
• /
• 2024

This study aims to talk about the necessity of solving the PFC gas emission problem raised by the recent development of the semiconductor industry and the remote plasma source method monitoring system used in the semiconductor industry. The 'monitoring system' means that the researchers applied machine learning to the existing monitoring technology and modeled it. In the process of this study, Residual Gas Analyzer monitoring technology and linear regression model were used. Through this model, the researchers identified emissions of at least 12700mg CO2 to 75800mg CO2 with values ranging from ion current 0.6A to 1.7A, and expect that the 'monitoring system' will contribute to the effective calculation of greenhouse gas emissions in the semiconductor industry in the future.

• Journal of the Semiconductor & Display Technology
• /
• v.23 no.3
• /
• pp.1-6
• /
• 2024

Recent research on embedded systems has become increasingly important due to their central role in high-performance embedded devices, including artificial intelligence, autonomous driving, and energy management technologies. Embedded systems are specialized computer systems designed to perform specific tasks while optimizing performance and minimizing memory usage. RISC-V, an open RISC-based instruction set architecture developed by the University of Berkeley in 2010, is well-suited for these systems. In addition to the base 32-bit integer instruction set, RISC-V supports extensions such as the M-extension for multiplication and division and the C-extension for instruction compression. In this paper, we propose the design of a 32-bit 5-stage pipeline RV32IMC processor aimed at high-performance embedded devices. By incorporating the RV32IMC instruction set, the proposed processor achieves enhanced computational efficiency and reduced code size, making it a strong candidate for energy-efficient, high-performance embedded applications. Furthermore, the design was validated on an Artix-7 field-programmable gate array, demonstrating the processor's feasibility and potential benefits for embedded systems.

• Journal of the Semiconductor & Display Technology
• /
• v.22 no.4
• /
• pp.53-58
• /
• 2023

The storage capacity of 3D-NAND flash memory has been enhanced by the multi-layer dielectrics. The deposition process has become more challenging due to the tight process margin and the demand for accurate process control. To reduce product costs and ensure successful processes, process diagnosis techniques incorporating artificial intelligence (AI) have been adopted in semiconductor manufacturing. Recently there is a growing interest in process diagnosis, and numerous studies have been conducted in this field. For higher model accuracy, various process and sensor data are required, such as optical emission spectroscopy (OES), quadrupole mass spectrometer (QMS), and equipment control state. Among them, OES is usually used for plasma diagnostic. However, OES data can be distorted by viewport contamination, leading to misunderstandings in plasma diagnosis. This issue is particularly emphasized in multi-dielectric deposition processes, such as oxide and nitride (ON) stack. Thus, it is crucial to understand the potential misunderstandings related to OES data distortion due to viewport contamination. This paper explores the potential for misunderstanding OES data due to data distortion in the ON stack process. It suggests the possibility of excessively evaluating process drift through comparisons with a QMS. This understanding can be utilized to develop diagnostic models and identify the effects of viewport contamination in ON stack processes.

• Journal of the Microelectronics and Packaging Society
• /
• v.30 no.4
• /
• pp.69-78
• /
• 2023

With the rapid growth of artificial intelligence, the demand for semiconductors is enormously increasing everywhere. To ensure the manufacturing quality and quantity simultaneously, the importance of automatic defect detection during the packaging process has been re-visited by adapting various deep learning-based methodologies into automatic packaging defect inspection. Deep learning (DL) models require a large amount of data for training, but due to the nature of the semiconductor industry where security is important, sharing and labeling of relevant data is challenging, making it difficult for model training. In this study, we propose a new framework for securing sufficient data for DL models with fewer computing resources through a divide-and-conquer approach. The proposed method divides high-resolution images into pre-defined sub-regions and assigns conditional labels to each region, then trains individual sub-regions and boundaries with boundary loss inducing the globally coherent and seamless images. Afterwards, full-size image is reconstructed by combining divided sub-regions. The experimental results show that the images obtained through this research have high efficiency, consistency, quality, and generality.