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arxiv: 2601.02053 · v2 · pith:2B7QWZ3Jnew · submitted 2026-01-05 · 💻 cs.AR · cs.SY· eess.SY

Ageing Monitoring for Commercial Microcontrollers Based on Timing Windows

Leandro Lanzieri , Jiri Kral , Goerschwin Fey , Holger Schlarb , Thomas C. Schmidt This is my paper

Pith reviewed 2026-05-16 18:13 UTC · model grok-4.3

classification 💻 cs.AR cs.SYeess.SY
keywords microcontroller ageinghardware degradationtiming windowssoftware self-testingmaximum operating frequencyembedded systems reliabilitytemperature effects
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The pith

A software-based method using variable timing windows can monitor hardware ageing in commercial microcontrollers by measuring shifts in maximum operating frequency.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a deployable technique for tracking degradation in microcontrollers used in embedded and dependable systems, where ageing can cause critical malfunctions. It replaces static guard bands, which limit performance and risk sudden failures, with a software self-test approach that applies timing windows of variable lengths to find each device's maximum operational frequency. Validation on real hardware shows the method consistently detects temperature-induced degradations reaching 13.79 percent across devices for a 60 degree Celsius increase. This allows field monitoring that could maintain higher performance while preventing timing errors as devices age.

Core claim

The authors follow a software-based self-testing approach that leverages timing windows of variable lengths to determine the maximum operational frequency of the devices, enabling detection of hardware degradation.

What carries the argument

Variable-length timing windows in software self-tests that probe the maximum operating frequency as a direct indicator of ageing-induced timing degradation.

Load-bearing premise

The measured changes in maximum operating frequency reflect permanent hardware ageing rather than transient temperature effects or other unmodeled factors.

What would settle it

Long-term operation of the same devices at constant temperature followed by re-measurement of maximum frequency; absence of a comparable frequency shift would falsify the ageing claim.

Figures

Figures reproduced from arXiv: 2601.02053 by Goerschwin Fey, Holger Schlarb, Jiri Kral, Leandro Lanzieri, Thomas C. Schmidt.

Figure 1
Figure 1. Figure 1: By observing the behaviour of payload execution at [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Higher frequencies give signal propagation shorter [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Maximum error-free frequency and its degradation with respect to the previous temperature step. Degradation values [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Evolution of execution errors with frequency at different temperatures. Payloads without transition are omitted. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Median execution time of the payloads at [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
read the original abstract

Microcontrollers are increasingly present in embedded deployments and dependable systems, for which malfunctions due to hardware ageing can have severe impact. The lack of deployable techniques for ageing monitoring on these devices has spread the application of guard bands to prevent timing errors due to degradation. Applying this static technique can limit performance and lead to sudden failures as devices age. In this paper, we follow a software-based self-testing approach to design monitoring of hardware degradation for microcontrollers. Deployable in the field, our technique leverages timing windows of variable lengths to determine the maximum operational frequency of the devices. We empirically validate the method on real hardware and find that it consistently detects temperature-induced degradations in maximum operating frequency of up to 13.79 % across devices for 60 {\deg}C temperature increase.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes a software-based self-test method that uses variable-length timing windows to measure the maximum operating frequency of commercial microcontrollers as a proxy for hardware ageing. It claims this approach is deployable in the field and empirically validates it on real hardware, reporting consistent detection of up to 13.79% reduction in maximum frequency for a 60°C temperature increase.

Significance. If the method can be shown to isolate cumulative, irreversible ageing effects (e.g., NBTI, HCI) from transient thermal variations, it would offer a practical, low-overhead technique for dynamic reliability monitoring in embedded systems, reducing reliance on conservative static guard bands. The real-hardware implementation is a strength, but current evidence primarily captures temperature sensitivity.

major comments (2)
  1. [Abstract and Experimental Validation] Abstract and Experimental Validation: The reported results measure maximum-frequency shifts under a 60°C ambient temperature increase, but provide no post-stress recovery measurements (cooling back to baseline) or constant-temperature long-term stress data to confirm irreversibility. This leaves the observed shifts attributable to reversible thermal delay changes rather than the irreversible ageing the method is asserted to monitor.
  2. [Experimental Validation] Experimental Validation: No information is given on the number of devices tested, statistical analysis (e.g., error bars, significance tests), or controls for confounding factors such as supply voltage variation or measurement noise, which are required to establish that the 13.79% figure reliably indicates ageing rather than experimental artifact.
minor comments (2)
  1. [Method] The manuscript should specify the exact software implementation of the timing windows (e.g., instruction sequences, measurement loop details) and how the maximum frequency is algorithmically determined from pass/fail outcomes.
  2. Add a dedicated limitations or future-work subsection discussing the distinction between temperature and ageing effects and planned extensions to time-based stress experiments.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the concerns about distinguishing reversible thermal effects from irreversible ageing and the need for additional experimental details. We will revise the manuscript to clarify the scope of our claims and strengthen the presentation of results.

read point-by-point responses

  1. Referee: [Abstract and Experimental Validation] The reported results measure maximum-frequency shifts under a 60°C ambient temperature increase, but provide no post-stress recovery measurements (cooling back to baseline) or constant-temperature long-term stress data to confirm irreversibility. This leaves the observed shifts attributable to reversible thermal delay changes rather than the irreversible ageing the method is asserted to monitor.

    Authors: We agree that the experiments demonstrate temperature-induced frequency shifts, which are expected to be largely reversible. The manuscript positions the timing-window technique as a field-deployable method to track maximum operating frequency, a direct indicator of timing degradation relevant to ageing mechanisms such as NBTI and HCI that are accelerated by elevated temperature. However, we did not include recovery or long-term constant-temperature stress data. In revision we will update the abstract, introduction, and discussion to explicitly state that the reported shifts reflect temperature sensitivity and to frame the method as a practical monitor whose outputs can be tracked over time in the field to detect cumulative irreversible effects. We will also note the value of future longitudinal studies for confirming irreversibility. revision: yes

  2. Referee: [Experimental Validation] No information is given on the number of devices tested, statistical analysis (e.g., error bars, significance tests), or controls for confounding factors such as supply voltage variation or measurement noise, which are required to establish that the 13.79% figure reliably indicates ageing rather than experimental artifact.

    Authors: The manuscript reports consistent results across multiple devices but does not currently provide the exact device count, error bars, or explicit controls. We will revise the experimental section to specify the number of devices, describe repeated measurement protocols used to assess repeatability, confirm that supply voltage was held at the nominal value with monitoring, and include basic statistical measures (standard deviation across trials) for the observed frequency shifts. These additions will substantiate that the 13.79% maximum shift is reproducible and not an artifact of noise or voltage variation. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical validation on hardware is independent of any fitted derivation

full rationale

The paper describes a software-based timing-window technique to measure maximum operating frequency on commercial MCUs and reports direct empirical observations of frequency shifts under controlled temperature changes. No equations, fitted parameters, or self-citations are presented that reduce the reported result to its own inputs by construction. The central claim rests on hardware measurements rather than any self-definitional loop, ansatz smuggled via citation, or renaming of a known result. This is the common case of a self-contained empirical study.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that timing-window failures directly indicate hardware degradation and that temperature-induced frequency shifts proxy long-term ageing. No free parameters or invented entities are mentioned in the abstract.

axioms (1)
  • domain assumption Timing-window test failures correspond to hardware degradation rather than transient effects.
    Invoked when the authors interpret frequency drops as ageing indicators.

pith-pipeline@v0.9.0 · 5443 in / 1173 out tokens · 32747 ms · 2026-05-16T18:13:17.921695+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

29 extracted references · 29 canonical work pages

  1. [1]

    Negative bias temperature instability: What do we understand?

    D. K. Schroder, “Negative bias temperature instability: What do we understand?”Microelec. Reliability, vol. 47, no. 6, pp. 841–852, 2007

    work page 2007

  2. [2]

    A. W. Strong, E. Y . Wuet al.,Hot Carriers. IEEE, 2009, ch. 5

    work page 2009

  3. [3]

    Studying the Degradation of Propaga- tion Delay on FPGAs at the European XFEL,

    L. Lanzieri, L. Butkowskiet al., “Studying the Degradation of Propaga- tion Delay on FPGAs at the European XFEL,” in27th Euromicro Conf. on Digital System Design (DSD). Paris, FR: IEEE, August 2024

    work page 2024

  4. [4]

    Ageing Analysis of Embedded SRAM on a Large-Scale Testbed Using Machine Learning,

    L. Lanzieri, P. Kietzmannet al., “Ageing Analysis of Embedded SRAM on a Large-Scale Testbed Using Machine Learning,” in26th Euromicro Conf. on Digital System Design (DSD). Durres, AL: IEEE, 2023

    work page 2023

  5. [5]

    Evaluation of Dynamic Frequency Control on an Automotive Microcontroller,

    A. Kaushik, S. Chumbalakaret al., “Evaluation of Dynamic Frequency Control on an Automotive Microcontroller,” in3rd Int. Conf. on Com- munication, Computing and Electronics Systems, V . Bindhu, J. M. R. S. Tavareset al., Eds. Singapore: Springer, 2022, pp. 313–327

    work page 2022

  6. [6]

    A Review of Techniques for Ageing Detection and Monitoring on Embedded Systems,

    L. Lanzieri, G. Martinoet al., “A Review of Techniques for Ageing Detection and Monitoring on Embedded Systems,”ACM Comput. Surv., vol. 57, no. 1, pp. 24:1–24:34, January 2025 2024

    work page 2025

  7. [7]

    A variation-resilient microprocessor with a two-level timing error detection and correction system in 28-nm CMOS,

    C.-Y . Hong and T.-T. Liu, “A variation-resilient microprocessor with a two-level timing error detection and correction system in 28-nm CMOS,” IEEE Jour . of Solid-State Circuits, vol. 55, no. 8, pp. 2285–2294, 2019

    work page 2019

  8. [8]

    Experimental Evaluation for Detecting Aging Effect on Microcontrollers based on Side-Channel Analysis,

    Y . Kaneko, Y . Hayashiet al., “Experimental Evaluation for Detecting Aging Effect on Microcontrollers based on Side-Channel Analysis,” in 2024 14th International Workshop on the Electromagnetic Compatibility of Integrated Circuits (EMC Compo). IEEE, Oct. 2024, pp. 1–5

    work page 2024

  9. [9]

    Counterfeit chip detection using scatter- ing parameter analysis,

    M. S. Safa, T. Mosaviriket al., “Counterfeit chip detection using scatter- ing parameter analysis,” in26th Int. Symp. on Design and Diagnostics of Electronic Circuits and Systems (DDECS). IEEE, 2023, pp. 99–104

    work page 2023

  10. [10]

    Microprocessor software-based self- testing,

    M. Psarakis, D. Gizopouloset al., “Microprocessor software-based self- testing,”IEEE Design & Test of Computers, vol. 27, pp. 4–19, 2010

    work page 2010

  11. [11]

    Online self tests for microcontrollers in safety related systems,

    T. Tamandl and P. Preininger, “Online self tests for microcontrollers in safety related systems,” in2007 5th IEEE International Conference on Industrial Informatics, vol. 1. IEEE, 2007, pp. 137–142

    work page 2007

  12. [12]

    Effective software-based self-test strategies for on-line periodic testing of embedded processors,

    A. Paschalis and D. Gizopoulos, “Effective software-based self-test strategies for on-line periodic testing of embedded processors,”IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 24, no. 1, pp. 88–99, 2005

    work page 2005

  13. [13]

    Fully automatic test program generation for microprocessor cores,

    F. Corno, G. Cumaniet al., “Fully automatic test program generation for microprocessor cores,” in2003 Design, Automation and Test in Europe Conference and Exhibition. IEEE, 2003, pp. 1006–1011

    work page 2003

  14. [14]

    A. W. Strong, E. Y . Wuet al.,Reliability wearout mechanisms in advanced CMOS technologies. John Wiley & Sons, 2009

    work page 2009

  15. [15]

    A. S. Sedra and K. C. Smith,Microelectronic circuits, 7th ed., ser. The Oxford series in electrical and computer engineering. New York: Oxford University Press, 2015

    work page 2015

  16. [16]

    On the efficiency of voltage over- scaling under temperature and aging effects,

    H. Amrouch, S. B. Ehsaniet al., “On the efficiency of voltage over- scaling under temperature and aging effects,”IEEE Transactions on Computers, vol. 68, no. 11, pp. 1647–1662, 2019

    work page 2019

  17. [17]

    Bubble razor: Eliminating timing margins in an ARM cortex-M3 processor in 45 nm CMOS using architecturally independent error detection and correction,

    M. Fojtik, D. Ficket al., “Bubble razor: Eliminating timing margins in an ARM cortex-M3 processor in 45 nm CMOS using architecturally independent error detection and correction,”IEEE Journal of Solid-State Circuits, vol. 48, no. 1, pp. 66–81, 2012

    work page 2012

  18. [18]

    Total ionizing dose effects on commercial arm microcontroller for low earth orbit satellite subsystems,

    H. Akah, D. Elfikyet al., “Total ionizing dose effects on commercial arm microcontroller for low earth orbit satellite subsystems,” inInternational Conference on Aerospace Sciences and Aviation Technology, vol. 17. The Military Technical College, 2017, pp. 1–8

    work page 2017

  19. [19]

    Total-Ionizing-Dose Induced Tim- ing Window Violations in CMOS Microcontrollers,

    Z. J. Diggins, N. Mahadevanet al., “Total-Ionizing-Dose Induced Tim- ing Window Violations in CMOS Microcontrollers,”IEEE Transactions on Nuclear Science, vol. 61, no. 6, pp. 2979–2984, Dec. 2014

    work page 2014

  20. [20]

    IEC 60730-1 — Automatic electrical controls — General require- ments,

    “IEC 60730-1 — Automatic electrical controls — General require- ments,” International Electrotechnical Commission, Tech. Rep., 2022

    work page 2022

  21. [21]

    March-based ram diagnosis algorithms for stuck-at and coupling faults,

    J.-F. Li, K.-L. Chenget al., “March-based ram diagnosis algorithms for stuck-at and coupling faults,” inInt. Test Conf.IEEE, 2001

    work page 2001

  22. [22]

    An analysis of fault effects and prop- agations in avr microcontroller atmega103 (l),

    A. Rohani and H. R. Zarandi, “An analysis of fault effects and prop- agations in avr microcontroller atmega103 (l),” in2009 Int. Conf. on Availability, Reliability and Security. IEEE, 2009, pp. 166–172

    work page 2009

  23. [23]

    Idealvolting: Reliable undervolting on wireless sensor nodes,

    U. Kulau, F. B ¨uschinget al., “Idealvolting: Reliable undervolting on wireless sensor nodes,”ACM Transactions on Sensor Networks (TOSN), vol. 12, no. 2, pp. 1–38, 2016

    work page 2016

  24. [24]

    Wolpert and P

    D. Wolpert and P. Ampadu,Managing Temperature Effects in Nanoscale Adaptive Systems. Springer New York, 2012

    work page 2012

  25. [25]

    A mosfet electron mobility model of wide temperature range (77-400 k) for ic simulation,

    K. Chain, J.-h. Huanget al., “A mosfet electron mobility model of wide temperature range (77-400 k) for ic simulation,”Semiconductor science and technology, vol. 12, no. 4, p. 355, 1997

    work page 1997

  26. [26]

    S. M. Sze and K. K. Ng,Physics of Semiconductor Devices, 3rd ed. John Wiley & Sons, 2007

    work page 2007

  27. [27]

    A unified mobility model for device simulation—II. Temperature dependence of carrier mobility and lifetime,

    D. Klaassen, “A unified mobility model for device simulation—II. Temperature dependence of carrier mobility and lifetime,”Solid-State Electronics, vol. 35, no. 7, pp. 961–967, 1992

    work page 1992

  28. [28]

    Impact of temperature fluctuations on circuit characteristics in 180nm and 65nm CMOS technologies,

    R. Kumar and V . Kursun, “Impact of temperature fluctuations on circuit characteristics in 180nm and 65nm CMOS technologies,” in International Symposium on Circuits and Systems. IEEE, 2006, p. 4

    work page 2006

  29. [29]

    Parameter variations and impact on circuits and microarchitecture,

    S. Borkar, T. Karniket al., “Parameter variations and impact on circuits and microarchitecture,” in40th Design Automation Conference. ACM, 2003, pp. 338–342

    work page 2003