Please use this identifier to cite or link to this item: https://doi.org/10.1021/acsanm.1c00960
Title: Transfer learning-based artificial intelligence-integrated physical modeling to enable failure analysis for 3 nanometer and smaller silicon-based CMOS transistors
Authors: JIEMING PAN 
LOW KAIN LU 
JOYDEEP GHOSH 
Senthilnath Jayavelu
Md Meftahul Ferdaus
Shang Yi Lim
Evgeny Zamburg 
Li Yida 
TANG BAOSHAN 
WANG XINGHUA 
LEONG JIN FENG 
Savitha Ramasamy
Tonio Buonassisi
Tham Chen Khong 
THEAN VOON YEW, AARON 
Keywords: failure analysis transistor bridge defect CMOS FinFET GAAFET technology computer-aided design (TCAD) machine learning transfer learning
Issue Date: 28-Jun-2021
Publisher: ACS Applied Nano Materials
Citation: JIEMING PAN, LOW KAIN LU, JOYDEEP GHOSH, Senthilnath Jayavelu, Md Meftahul Ferdaus, Shang Yi Lim, Evgeny Zamburg, Li Yida, TANG BAOSHAN, WANG XINGHUA, LEONG JIN FENG, Savitha Ramasamy, Tonio Buonassisi, Tham Chen Khong, THEAN VOON YEW, AARON (2021-06-28). Transfer learning-based artificial intelligence-integrated physical modeling to enable failure analysis for 3 nanometer and smaller silicon-based CMOS transistors. ACS Applied Nano Materials. ScholarBank@NUS Repository. https://doi.org/10.1021/acsanm.1c00960
Rights: Attribution-NoDerivatives 4.0 International
Abstract: Integral to the success of the semiconductor industry in keeping up with Moore’s law is the importance of failure analysis (FA). Accurate and fast FA is vital in ensuring yield, reliability, and rapid production in the semiconductor industry. However, locating defects among tens of billions of transistors packed in the tiny modern microchip is not a trivial task. Not only the process technology has to achieve such high integration of devices evolved to become astoundingly sophisticated but also debugging for defects in these chips has become remarkably complex. With electrical nanoprobing, we show how artificial intelligence-integrated physical modeling can be effective in finding difficult proverbial needle-in-a-haystack defects based on the electrical responses of the devices. Moreover, the information learned on current devices can be transferred to the latest transistor technologies, enabling machine learning-based defect sleuths of the future. Notably, we achieved a defect region classification accuracy of 99.5% on well-studied fin nanoscale field-effect transistors (FET) using a defect dataset based on an experimentally calibrated TCAD digital twin model and an adaptive boundary refinement technique. With transfer learning, a defect region classification accuracy of 99.58% is also achieved on the next-generation gate-all-around FETs, overcoming the lack of crucial datasets and optimized labels to guide and accelerate the production process of emerging devices. The proposed technique is expected to be the next level of defect identification, an important stepping stone to accelerate the production process for advanced technology nodes beyond 3 nm.
Source Title: ACS Applied Nano Materials
URI: https://scholarbank.nus.edu.sg/handle/10635/218176
ISSN: 2574-0970
DOI: 10.1021/acsanm.1c00960
Rights: Attribution-NoDerivatives 4.0 International
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