Use of Nanoprobing as the Diagnostic Tool for Nanoscaled Devices

Toh, S.L.; Mai, Zhihong; Tan, Puay Siew; Hendarto, E.; Tan, H.; Wang, Q.F.; Cai, Jiaxiang; Deng, Qiyun; Ng, Ting Hui; Goh, Y.W.; Lam, Jeffrey; Hsia, L. C.

doi:10.1109/ipfa.2007.4378057

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…The methods of FA have improved over the years, owing to the rapid development of advanced device analysis tools such as E-beam absorbed current, scanning transmission electron microscopy (STEM), − transmission electron microscopy (TEM), − nanoprobing, − laser voltage probing, photoemission electron microscopy, − and many more. , These tools provide critical information to help experts accurately identify the failure mode and mechanism, as shown in Figure b(iv,v).…”

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confidence: 99%

Transfer Learning-Based Artificial Intelligence-Integrated Physical Modeling to Enable Failure Analysis for 3 Nanometer and Smaller Silicon-Based CMOS Transistors

Pan

Low

Ghosh

et al. 2021

ACS Appl. Nano Mater.

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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-allaround 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.

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confidence: 99%

Transfer Learning-Based Artificial Intelligence-Integrated Physical Modeling to Enable Failure Analysis for 3 Nanometer and Smaller Silicon-Based CMOS Transistors

Pan

Low

Ghosh

et al. 2021

ACS Appl. Nano Mater.

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“…(μm). Nano-probing is becoming an increasingly critical tool for identifying non-visual failures via electrical characterization in current electrical FA metrology for fault isolation [3][4][5][6]. EBAC which is one of the functions of a stateof-the-art nano-probing system has been introduced and widely used for FA on semiconductor devices [7][8][9].…”

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confidence: 99%

Nanoprobing EBAC technique to reveal the failure root cause of gate oxide reliability issues of an IC process

Tan

Dawood

Low

et al. 2014

2014 IEEE International Integrated Reliability Workshop Final Report (IIRW)

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Reliability tests, such as High-Temperature Operating Life (HTOL), Hot Carrier Injection (HCI), Time Dependent Dielectric Breakdown (TDDB), etc., is required for the lifetime prediction of an integrated circuit (IC) product. Transmission Electron Microscopy (TEM) analysis is required to provide insights to the defect mechanisms, induced in the scaled gate oxide, by the above reliability tests. In this paper, application of high resolution Nano-probing Electron Beam Absorbance Current (EBAC) to isolate the defective locations for TEM analysis was investigated. We have successfully demonstrated that TEM analysis after Nano-probing EBAC fault isolation is an effective technique to reveal the failure root cause of gate oxide breakdown after reliability stresses.

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