• Journal of the Microelectronics and Packaging Society
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• v.30 no.3
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• pp.11-19
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• 2023

Recently with the rapid advancement of artificial intelligence (AI) technologies such as Chat GPT, AI semiconductors have become important. AI technologies require the ability to process large volumes of data quickly, as they perform tasks such as big data processing, deep learning, and algorithms. However, AI semiconductors encounter challenges with excessive power consumption and data bottlenecks during the processing of large-scale data. Thus, the latest packaging technologies are required for AI semiconductor computations. In this study, the authors have described packaging technologies applicable to AI semiconductors, including interposers, Through-Silicon-Via (TSV), bumping, Chiplet, and hybrid bonding. These technologies are expected to contribute to enhance the power efficiency and processing speed of AI semiconductors.

• Electronics and Telecommunications Trends
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• v.35 no.7
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• pp.13-22
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• 2020

There is no denying that computing power has been a crucial driving force behind the development of artificial intelligence today. In addition, artificial intelligence (AI) semiconductors and computing systems are perceived to have promising industrial value in the market along with rapid technological advances. Therefore, success in this field is also meaningful to the nation's growth and competitiveness. In this context, ETRI's AI strategy proposes implementation directions and tasks with the aim of strengthening the technological competitiveness of AI semiconductors and computing systems. The paper contains a brief background of ETRI's AI Strategy #2, research and development trends, and key tasks in four major areas: 1) AI processors, 2) AI computing systems, 3) neuromorphic computing, and 4) quantum computing.

• Smart Media Journal
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• v.13 no.3
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• pp.36-44
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• 2024

Recently, large-scale artificial intelligence (AI) such as ChatGPT have been developed, and as AI is used across various industrial fields, attention is focused on AI chips (semiconductors). AI chips refer to chips designed for calculations for AI algorithms, and many companies at domestic and abroad, such as NVIDIA, Tesla, and ETRI, are developing AI chips. In this paper, we survey research trends on nine types of AI chips. Currently, many attempts have been made to improve the computational performance of most AI chips, and semiconductors for specific purposes are also being designed. In order to compare various AI semiconductors, each chip is analyzed in terms of operation unit, speed, power, and energy efficiency. We introduce currently existing optimization methodologies for AI computation. Based on this, future research directions for AI semiconductors are presented in this paper.

• Electronics and Telecommunications Trends
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• v.39 no.2
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• pp.66-76
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• 2024

Artificial-intelligence (AI) semiconductors are crucial for securing national core competitiveness, including dominating the AI and data ecosystem and succeeding in the Digital New Deal. When examining the macroenvironment, the global division of labor in the semiconductor industry has weakened owing to the technological competition between the United States and China. Major countries are aiming to build the entire semiconductor ecosystem around their territories. As a result, these countries are formulating policy goals tailored to their realities and actively pursuing key policies such as research and development, securing manufacturing bases, workforce development, and financial support. These policies also focus on intercountry cooperation and bold government policy support, which is deemed essential. To secure core competitiveness in AI semiconductors, South Korea needs to examine the policy directions of major countries and actively formulate and implement policies for this semiconductor industry.

• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.1-11
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• 2023

The emergence of generative hyperscale artificial intelligence (AI) has enabled new services, such as image-generating AI and conversational AI based on large language models. Such services likely lead to the influx of numerous users, who cannot be handled using conventional AI models. Furthermore, the exponential increase in training data, computations, and high user demand of AI models has led to intensive hardware resource consumption, highlighting the need to develop domain-specific semiconductors for hyperscale AI. In this technical report, we describe development trends in technologies for hyperscale AI processors pursued by domestic and foreign semiconductor companies, such as NVIDIA, Graphcore, Tesla, Google, Meta, SAPEON, FuriosaAI, and Rebellions.

• Electronics and Telecommunications Trends
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• v.33 no.6
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• pp.24-33
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• 2018

In the age of IoT, in which everything is connected to a network, there have been increases in the amount of data traffic, latency, and the risk of personal privacy breaches that conventional cloud computing technology cannot cope with. The idea of edge computing has emerged as a solution to these issues, and furthermore, the concept of ultra-low power edge intelligent semiconductors in which the IoT device itself performs intelligent decisions and processes data has been established. The key elements of this function are an intelligent semiconductor based on artificial intelligence, connectivity for the efficient connection of neurons and synapses, and a large-scale spiking neural network simulation framework for the performance prediction of a neural network. This paper covers the current trends in ultra-low power edge intelligent semiconductors including issues regarding their technology and application.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.2
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• pp.16-22
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• 2024

With advances of artificial intelligence (AI) technology, the demand is increasing for high-end semiconductors in various places such as data centers. In order to improve the performance of semiconductors, reducing the pitch of patterns and increasing density of I/Os are required. For this issue, 2.5dimension(D) packaging is gaining attention as a promising solution. The core technologies used in 2.5D packaging include microbump, interposer, and bridge die. These technologies enable the implementation of a larger number of I/Os than conventional methods, enabling a large amount of information to be transmitted and received simultaneously. This paper proposes the Molded Bridge die on Substrate (MBoS) process technology, which combines molding and Redistribution Layer (RDL) processes. The proposed MBoS technology is expected to contribute to the popularization of next-generation packaging technology due to its easy adaption and wide application areas.

• Journal of Adhesion and Interface
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• v.25 no.2
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• pp.43-49
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• 2024

In recent years, the flexible organic synaptic transistor (FOST) has garnered attention for its flexibility, biocompatibility, ease of processability, and reduced complexity, which arise from using organic semiconductors as channel layers. These transistors can emulate the plasticity of the human brain with a simpler structure and lower fabrication costs compared to conventional inorganic synaptic devices. This makes them suitable for applications in next-generation wearable devices and soft robotics technologies. In FOST, the organic substrate is sensitive to the device preparation temperature; high-temperature treatment processes can cause thermal deformation of the organic substrate. Therefore, low-temperature solution-based processing techniques are essential for fabricating high-performance devices. This review summarizes the current research status of low-temperature solution-based FOST devices and presents the problems and challenges that need to be addressed.

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.139-161
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• 2024

The Fourth Industrial Revolution has spurred widespread adoption of AI-based services, driving global interest in AI semiconductors for efficient large-scale computation. Text mining research, historically using LDA, has evolved with machine learning integration, exemplified by the 2021 BERTopic technology. This study employs BERTopic to analyze AI semiconductor-related patents and research data, generating 48 topics from 2,256 patents and 40 topics from 1,112 publications. While providing valuable insights into technology trends, the study acknowledges limitations in taking a macro approach to the entire AI semiconductor industry. Future research may explore specific technologies for more nuanced insights as the industry matures.

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