AI 반도체 패키징을 위한 TGV 및 Cu 충전 연구의 최근 동향
Recent Research Trends in TGV and Cu-Filling for AI Semiconductor Packaging
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초록
GPU, TPU, NPU와 같은 AI 반도체는 ChatGPT와 같은 딥러닝 모델에 필수적이며, 고대역폭 메모리 (HBM) 가 성능을 향상시킨다. HBM 시장은 2022년부터 2029년까지 연평균 46%씩 성장할 것으로 예상된다. 반도체 패키징의열방출 및 신호 간섭과 같은 문제를 개선하기 위해서 3D-IC 스태킹 및 인터포저 기술이 사용되고 있다. 실리콘 인터포저는 몇 가지 문제를 겪고 있지만, Through Glass Via (TGV) 기술을 사용한 유리 인터포저는 기존의 Through Silicon Via (TSV) 보다 열관리, 신호 무결성, 비용의 효율성이 높다. 본 고에서는 AI 반도체의 신뢰성 및 성능향상을 위한 코어기술들인 TGV의 선택적 레이저 식각 (Selective Laser Etching), 기능층 증착 및 펄스 전해도금 등 TGV 제조 기술의 최근 연구동향에 대하여 서술하였다. SLE 기술을 통하여 레이저와 에칭 용액 (etchant)을 사용하여 기판에서 재료를 선택적으로제거하여 유리에 비아를 형성할 수 있다.
Abstract
AI semiconductors like GPUs, TPUs, and NPUs are crucial for deep learning models, with High Bandwidth Memory (HBM) enhancing performance. The HBM market is projected to grow annually by 46% from 2022 to 2029. To address challenges in semiconductor packaging, such as heat dissipation and signal interference, 3D-IC stacking and glass interposers with Through Glass Via (TGV) technology are emerging as key solutions. TGV improves thermal management, signal integrity, and cost efficiency. This paper discusses recent research trends in TGV manufacturing technologies, such as selective laser etching (SLE), functional layer deposition, and pulse electroplating, which are core technologies for enhancing the reliability and performance of AI semiconductors. SLE technology allows the formation of vias in glass by selectively removing material from a substrate using a laser and an etchant.
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