Abstract
Scan structures, which are widely used in cryptographic circuits for wireless sensor networks applications, are essential for testing very-large-scale integration (VLSI) circuits. Faults in cryptographic circuits can be effectively screened out by improving testability and test coverage using a scan structure. Additionally, scan testing contributes to yield improvement by identifying fault locations. However, faults in circuits cannot be tested when a fault occurs in the scan structure. Moreover, various defects occurring early in the manufacturing process are expressed as faults of scan chains. Therefore, scan-chain diagnosis is crucial. However, it is difficult to obtain a sufficiently high diagnosis resolution and accuracy through the conventional scan-chain diagnosis. Therefore, this article proposes a novel scan-chain diagnosis method using regression and fan-in and fan-out filters that require shorter training and diagnosis times than existing scan-chain diagnoses do. The fan-in and fan-out filters, generated using a circuit logic structure, can highlight important features and remove unnecessary features from raw failure vectors, thereby converting the raw failure vectors to fan-in and fan-out vectors without compromising the diagnosis accuracy. Experimental results confirm that the proposed scan-chain-diagnosis method can efficiently provide higher resolutions and accuracies with shorter training and diagnosis times.
Highlights
Wireless sensor networks (WSNs) are composed of several sensor nodes that are deployed in the area to be monitored, through certain topologies and for certain purposes
The accuracy, training time, and diagnosis time of the proposed scan-chain diagnosis were evaluated through the experiments
The ITC’99 benchmark, which represents a wide range of industry-representative circuits and was used in this study, openrisc1200 (OR1200) and advanced encryption standard (AES) circuit are summarized in Table 4 [40]
Summary
Wireless sensor networks (WSNs) are composed of several sensor nodes that are deployed in the area to be monitored, through certain topologies and for certain purposes. Through some suitable methods and their respective information exchanges, WSNs collaboratively perceive physical world information and collect and collate the information of perceived objects within the network coverage area [1,2]. Because of these characteristics, WSNs have been widely used for various environmental, health, military, and commercial applications, such as intelligent transportation, smart homes, industrial monitoring, logistics, and healthcare systems [3,4]. The public key is freely available to anyone, whereas the private key is maintained secure and is only known to its owner These cryptography algorithms require numerous modular multiplication and exponentiation operations, which are computationally expensive. Specific circuits for cryptographic algorithms that require high computational power have been designed [8]
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