Samsung Embraces AI and Big Data, Revolutionizes Chipmaking Process

Samsung Electronics Co., the world’s leading memory chip manufacturer, is set to revolutionize its chipmaking process using cutting-edge artificial intelligence (AI) and big data technology. This initiative aims to boost productivity, refine product quality, and compete with its major foundry rival, Taiwan Semiconductor Manufacturing Co. With the Samsung Advanced Institute of Technology (SAIT) and the Device Solutions (DS) division at the forefront, the entire chipmaking process will undergo a remarkable transformation.

Also Read: How Did A Taiwanese Company Become The Backbone Of Modern AI?

Enhancing Productivity and Quality with AI and Big Data

Samsung’s semiconductor business, led by President Kyung Kye-hyun, plans to leverage AI & big data technology to achieve two key objectives. Firstly, the company aims to enhance wafer manufacturing yields, closing the gap with Taiwan Semiconductor Manufacturing Co. Secondly, Samsung seeks to optimize productivity and refine the quality of its chip products. By harnessing the potential of AI, the company aims to gain valuable insights into the chipmaking process and make data-driven decisions for improved outcomes.

Also Read: Microsoft & AMD Set To Challenge NVIDIA in AI Chip Manufacturing

Widening the Scope of AI in Chipmaking

The collaboration between Samsung’s SAIT & DS divisions is leading to the widespread adoption of AI technology throughout the chipmaking process. In various stages, Samsung will employ AI tech, including DRAM design automation, chip material development, foundry yield improvement, mass production, and chip packaging. This comprehensive approach demonstrates Samsung’s commitment to infusing AI across the entire chipmaking ecosystem.

Unleashing AI’s Potential: Overcoming Challenges and Defects

To maximize the potential of AI, Samsung is actively recruiting AI experts from renowned universities & prominent tech companies, domestically & abroad. The company’s AI technology will be crucial in identifying the causes of unnecessary wafer losses, optimizing manufacturing processes, and analyzing DRAM product defects. By leveraging AI, Samsung aims to address critical challenges that arise from ultra-fine chipmaking processes. This includes processes such as transistor interference and leakage current, which can impact production yields.

Racing Towards Technological Advancement

Samsung’s ambitious move to apply artificial intelligence to chip manufacturing is well-timed, as chipmakers worldwide strive to push the boundaries of chip processing nodes. Samsung and Taiwan Semiconductor Manufacturing Co. compete fiercely to lead the industry with state-of-the-art 2 to 3-nanometer transistor process technology. Samsung has set its sights on commercializing a 2-nanometer transistor process technology by 2025, further intensifying the rivalry.

Also Read: Microsoft & AMD Set To Challenge NVIDIA in AI Chip Manufacturing

Collaborating for AI Advancement

Recognizing the significance of collaboration, Samsung’s SAIT is working hand-in-hand with the DS division to develop an AI-based automated chip manufacturing system, data learning algorithms, and related software. Additionally, Samsung & Naver Corp. have embarked on a joint effort to create a generative artificial intelligence platform for corporate users. This partnership aims to compete with global AI tools, including popular offerings like ChatGPT.

Also Read: Microsoft and OpenAI Clash Over AI Integration

Our Say

Samsung’s decision to integrate AI & big data technology into its chipmaking process marks a pivotal moment in the semiconductor industry. By embracing the potential of AI, the company aims to enhance productivity, refine product quality, & compete on a global scale. As the world eagerly watches the advancements in chip processing nodes, Samsung’s strategic move sets the stage for a new era of innovation, unlocking limitless possibilities in the realm of chip manufacturing.

K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.