Aging Adaption in Integrated Circuits Using a Novel Built-In Sensor

Wang, Xiaoxiao; Winemberg, LeRoy; Su, Donglin; Tran, Dat; George, Saji; Ahmed, Nisar; Palosh, Steve; Dobin, Allan; Tehranipoor, Mohammad

doi:10.1109/tcad.2014.2366876

Cited by 18 publications

(3 citation statements)

References 29 publications

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“…In addition to the common practice aimed at mitigating sensor aging (e.g. anti-oxidation coating), Wang et al [31] designed a new sensor that can measure the degree of aging for crystal vibrating materials and proposed a method to compensate the effects of aging accordingly. However, their method relies on the measurement provided by their new sensor and is thus only applicable to crystal vibrating materials.…”

Section: Related Workmentioning

confidence: 99%

Robust Elbow Angle Prediction With Aging Soft Sensors via Output-Level Domain Adaptation

et al. 2021

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Wearable devices equipped with soft sensors provide a promising solution for body movement monitoring. Specifically, body movements like elbow flexion can be captured by monitoring the stretched soft sensors' resistance changes. However, in addition to stretching, the resistance of a soft sensor is also influenced by its aging, which makes the resistance a less stable indicator of the elbow angle. In this paper, we leverage the recent progress in Deep Learning and address the aforementioned issue by formulating the aging-invariant prediction of elbow angles as a domain adaption problem. Specifically, we define the soft sensor data (i.e., resistance values) collected at different aging levels as different domains and adapt a regression neural network among them to learn domain-invariant features. However, unlike the popular pairwise domain adaptation problem that only involves one source and one target domain, ours is more challenging as it has "infinite" target domains due to the nonstop aging. To address this challenge, we novelly propose an output-level domain adaptation approach which builds on the fact that the elbow angles are in a fixed range regardless of aging. Experimental results show that our method enables robust and accurate prediction of elbow angles with aging soft sensors, which significantly outperforms supervised learning ones that fail to generalize to aged sensor data.

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Section: Related Workmentioning

confidence: 99%

Robust Elbow Angle Prediction With Aging Soft Sensors via Output-Level Domain Adaptation

et al. 2021

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“…In another scheme [51], device ageing due to NBTI is countered through standard-cell sensor-facilitated measurement of frequency degradation. It is followed by inducing additional timing margin for the critical path to prevent device failure due to continued ageing.…”

Section: Comparative Analysis With Contemporary Mitigation Technmentioning

confidence: 99%

Ingress of Threshold Voltage-Triggered Hardware Trojan in the Modern FPGA Fabric–Detection Methodology and Mitigation

et al. 2020

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The ageing phenomenon of negative bias temperature instability (NBTI) continues to challenge the dynamic thermal management of modern FPGAs. Increased transistor density leads to thermal accumulation and propagates higher and non-uniform temperature variations across the FPGA. This aggravates the impact of NBTI on key PMOS transistor parameters such as threshold voltage and drain current. Where it ages the transistors, with a successive reduction in FPGA lifetime and reliability, it also challenges its security. The ingress of threshold voltage-triggered hardware Trojan, a stealthy and malicious electronic circuit, in the modern FPGA, is one such potential threat that could exploit NBTI and severely affect its performance. The development of an effective and efficient countermeasure against it is, therefore, highly critical. Accordingly, we present a comprehensive FPGA security scheme, comprising novel elements of hardware Trojan infection, detection, and mitigation, to protect FPGA applications against the hardware Trojan. Built around the threat model of a naval warship's integrated self-protection system (ISPS), we propose a threshold voltage-triggered hardware Trojan that operates in a threshold voltage region of 0.45V to 0.998V, consuming ultra-low power (10.5nW), and remaining stealthy with an area overhead as low as 1.5% for a 28 nm technology node. The hardware Trojan detection sub-scheme provides a unique lightweight threshold voltage-aware sensor with a detection sensitivity of 0.251mV/nA. With fixed and dynamic ring oscillatorbased sensor segments, the precise measurement of frequency and delay variations in response to shifts in the threshold voltage of a PMOS transistor is also proposed. Finally, the FPGA security scheme is reinforced with an online transistor dynamic scaling (OTDS) to mitigate the impact of hardware Trojan through run-time tolerant circuitry capable of identifying critical gates with worst-case drain current degradation. INDEX TERMS Ageing mechanism, field programmable gate array (FPGA), hardware Trojan, negative bias temperature instability (NBTI), propagation delay, reliability, threshold voltage. I. INTRODUCTION A modern FPGA is not merely an emulator but a hardware accelerator with heterogenous hard IP cores, such as complex memory blocks, multiple processors, and DSP blocks. Systems on chip (SoC), network on chip (NoC), and adaptive compute acceleration platform (ACAP) are the significant performance and functional enhancements of FPGAs, that have been made possible due to the continual shrinking of transistor sizes down to the scales of 10 nm and below. The performance benefits, however, are limited by power The associate editor coordinating the review of this manuscript and approving it for publication was Luis Javier Garcia Villalba. and timing closures. Similarly, the geometric structures of FPGAs with much less silicon and relatively more oxide and moulding compound complicate the heat conduction paths [1]. On the one hand, where it may deteriorate the worstcase heat dissi...

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“…Data analysis and machine learning methods have been used to recover chip identification [80,81]. On-chip aging sensors are a popular solution for counterfeit chip isolation [82]. Another emerging problem is IC overproduction and IP piracy [83][84][85], which occurs primarily because of lack of oversight and/or direct involvement in fabrication after passing the design over to the foundry.…”

Section: Counterfeiting Prevention and Ic Protectionmentioning

confidence: 99%

Introduction to Hardware Security

Jin

2015

Electronics

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Hardware security has become a hot topic recently with more and more researchers from related research domains joining this area. However, the understanding of hardware security is often mixed with cybersecurity and cryptography, especially cryptographic hardware. For the same reason, the research scope of hardware security has never been clearly defined. To help researchers who have recently joined in this area better understand the challenges and tasks within the hardware security domain and to help both academia and industry investigate countermeasures and solutions to solve hardware security problems, we will introduce the key concepts of hardware security as well as its relations to related research topics in this survey paper. Emerging hardware security topics will also be clearly depicted through which the future trend will be elaborated, making this survey paper a good reference for the continuing research efforts in this area.

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Aging Adaption in Integrated Circuits Using a Novel Built-In Sensor

Cited by 18 publications

References 29 publications

Robust Elbow Angle Prediction With Aging Soft Sensors via Output-Level Domain Adaptation

Robust Elbow Angle Prediction With Aging Soft Sensors via Output-Level Domain Adaptation

Ingress of Threshold Voltage-Triggered Hardware Trojan in the Modern FPGA Fabric–Detection Methodology and Mitigation

Introduction to Hardware Security

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