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Title: Significance of activation functions in developing an online classifier for semiconductor defect detection
Authors: Md Meftahul Ferdaus
Bangjian Zhou
Ji Wei Yoon
Kain Lu Low 
Jieming Pan 
Joydeep Ghosh 
Min Wu
Xiaoli Li
Aaron Voon-Yew Thean 
J. Senthilnath
Keywords: Leaky ReLU
Online learning
Defect detection
Issue Date: 21-Apr-2022
Publisher: Elsevier
Citation: Md Meftahul Ferdaus, Bangjian Zhou, Ji Wei Yoon, Kain Lu Low, Jieming Pan, Joydeep Ghosh, Min Wu, Xiaoli Li, Aaron Voon-Yew Thean, J. Senthilnath (2022-04-21). Significance of activation functions in developing an online classifier for semiconductor defect detection. Knowledge-Based Systems 248 : 108818. ScholarBank@NUS Repository.
Abstract: In anomaly detection problems for advanced semiconductor devices, non-visual defects occur frequently. Machine learning (ML) algorithms have the advantage of identifying such defects. However, in this real-world problem, data comes sequentially in a streaming fashion, thus, we may not have sufficient data to train an ML model in batch mode. In such a scenario, online ML models are useful to detect defects immediately since they work in a single-pass mode. Besides, when data is collected from more realistic non-stationary monitoring environments, online ML models with evolving architecture are more practical. Thus, evolving and online ML models are developed in this work to detect defects in technology computer-aided design (TCAD)-based digital twin model of advanced nano-scaled semiconductor devices such as a fin field-effect transistor (FinFET) and a gate-all-around field-effect transistor (GAA-FET). Activation functions (AFs) in deep neural networks (DNNs) and membership functions (MFs) in neuro-fuzzy systems (NFSs) play an important role in the performance of those ML models. This work focuses on analyzing the effects of various AFs/MFs in our developed online ML models while detecting defects in real-world nano-scaled semiconductor devices, where significant training samples are not available. From various semiconductor datasets having fewer samples, it has been observed that the proposed evolving neuro-fuzzy system (ENFS) with Leaky-ReLU MF performs better (improvement in the range of 1.9% to 30.8% considering overall classification accuracy) than the other DNN or ENFS-based online ML models. Having an evolving architecture and online learning mechanism, besides anomaly detection, the proposed model’s performance has also been evaluated for handling large data streams problems with concept drift. The performance of the proposed method has been compared with some recently developed baselines under the prequential test-then-train protocol. The classification rates of the proposed method has an improvement in the range of 1.1% to 65.9% than the existing methods.
Source Title: Knowledge-Based Systems
ISSN: 0950-7051
Appears in Collections:Staff Publications

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