DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Kim, Joungho | - |
dc.contributor.advisor | 김정호 | - |
dc.contributor.author | Hwang, In-Tae | - |
dc.date.accessioned | 2022-04-27T19:31:02Z | - |
dc.date.available | 2022-04-27T19:31:02Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=963406&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/295950 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2021.8,[iii, 39 p. :] | - |
dc.description.abstract | In this paper, we propose a deep neural network (DNN)-based potential defect detection and classification method for through silicon via (TSV) in 2.5D & 3D packaging. In order to overcome the limitations of the existing analysis method, this paper applied the signal integrity analysis method to search for potential defects that cause quality problems, and additionally applied a deep neural network (DNN) to confirm that it is a faster and more accurate method than the existing analysis method. To verify the proposed method, the level and location of potential defects were applied by type to the structure to which the through silicon via was applied, and the results obtained through the signal integrity analysis method and the deep neural network were compared. In addition, the effectiveness of the proposed method was confirmed by applying the proposed method to high bandwidth memory (HBM), which is a representative package with through silicon via. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Through Silicon Via (TSV)▼aSignal Integrity▼aDeep Neural Network (DNN)▼aPotential Defect▼a2.5D & 3D Package | - |
dc.subject | 관통 실리콘 비아▼a신호 무결성▼a심층 신경망▼a잠재적인 불량▼a2.5차원 및 3차원 패키지 | - |
dc.title | Deep neural network (DNN)-based potential defect detection and classification method for through silicon via (TSV) in 2.5D & 3D packaging | - |
dc.title.alternative | 관통 실리콘 비아가 적용된 2.5 & 3차원 패키지에 대한 심층 신경망 기반의 잠재적인 결함 탐색 및 분류 방법 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | 황인태 | - |
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