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REVIEW 4 major objections 5 minor 45 references

Toward Memristor-like Resonant Sensors: Observation of Pinched Hysteresis within MEMS Resonators

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A MEMS resonator shows memristor-like pinched hysteresis between a stiffness input and its electrical output.

desk verdict First pinched hysteresis in a MEMS resonator, measured through a PLL; the observation is plausible and new, but the memristor claim needs controls against loop-controller artifacts. read the letter →

arxiv 2506.01749 v1 pith:CBUZ53BX submitted 2025-06-02 physics.app-ph

Erion Uka , Chun Zhao This is my paper
classification physics.app-ph
keywords MEMSresonatorpinchedhysteresismemristorparametricmodulationphase-lockedloopmodecouplingMemReSensor
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper reports the first observation of pinched hysteresis — the self-crossing loop that defines a memristor — in a silicon MEMS resonator, with no material switching involved. The device is a generic double-ended tuning-fork resonator whose vibration modes are virtually coupled by parametric pump signals; a phase-locked loop tracks its resonance while a slow electrical offset voltage perturbs the stiffness. Sweeping this stiffness input up and down produces hysteresis loops in the output frequency and amplitude, and the loops cross without exchanging branches, which is precisely the pinched-hysteresis fingerprint. The authors argue that this makes resonant sensors capable of volatile memory of past input and programmable sensitivity, and could lead to sensors that compute within the physical domain.

What carries the argument

The load-bearing mechanism is parametric modulation (red-detuned pumping) combined with phase-locked-loop tracking. Parametric pumps at frequencies near the differences between the first mode and higher modes, $f_p^{1,j}=f_j-f_1+\Delta f_p^{1,j}$, virtually couple the modes, modifying the phase transition from $180^\circ$ to $0^\circ$ so that it crosses the $90^\circ$ set-point multiple times. As the electrostatic offset voltage $v_{\text{offset}}$ changes the stiffness, these phase crossings move; when two crossings merge, the PLL discontinuously switches modes. The set of branches traced by upward and downward sweeps, with crossings that do not allow branch exchange, constitutes the pinched hysteresis. The paper also uses the conversion $\Delta k_{e,1}/\Delta v_{\text{offset}} \approx 0.126\ \mathrm{N/m/V}$ from electrostatic parallel-plate formulas, validated against the experimental value $0.121\ \mathrm{N/m/V}$, to state the input in stiffness units.

What would settle it

Repeat the closed-loop stiffness sweep at several rates (for example 1 mHz to 1 Hz) and with different PLL bandwidth settings; if the loop area, crossing location, or discontinuity voltages change systematically with rate or controller gain, the pinched hysteresis is partly a control-loop artefact. Alternatively, map the open-loop phase response as a function of offset voltage and verify that the number of phase crossings changes exactly at the measured discontinuity voltages; if it does not, the PLL itself is generating the bistability.

Watch

Extended reading notes

Core claim

The central claim is that the pinched hysteresis fingerprint can be transferred from the electrical domain into a mixed physical-electrical domain: an electrostatic stiffness perturbation $\Delta k_{e,1}$ acts as the input and the resonator's tracked frequency or amplitude acts as the output, without invoking a memristive material. Under the parametric-modulation operating scheme, a pump at $f_p^{1,j}=f_j-f_1+\Delta f_p^{1,j}$ creates virtual coupling between mode 1 and mode j. This changes the phase response near $f_1$ so that the $90^\circ$ phase set-point of the phase-locked loop is crossed at one or three frequencies depending on stiffness. At two stiffness values the three crossings degenerate to one; the PLL then jumps to another coupled mode, and the upward and downward sweeps follow different branches. The branches cross at zero offset voltage but the device does not switch there — the defining property of a pinched hysteresis loop. With one pump this appears in the amplitude response; with two pumps, in the frequency response; with three pumps, multiple pinched loops appear in both, because intermediate branches are accessible in either sweep direction.

Load-bearing premise

The load-bearing assumption is that the phase-locked loop, locked to a fixed $90^\circ$ phase set-point, follows the resonator's mode branches quasi-statically as the 10 mHz offset-voltage sweep changes stiffness, so the recorded frequency and amplitude curves are the resonator's intrinsic response rather than a product of the PLL/PID controller dynamics or sweep speed.

Editorial extensions

If this is right

  • A resonant sensor can hold two or more stable sensitivity values at the same input, with the past input deciding which one is active; this is a volatile, mechanical memory of the sort memristors provide.
  • With two or three parametric pumps, the device shows multiple pinched hysteresis loops, so a single resonator can encode multi-state input–output relations rather than a single fixed sensitivity.
  • Because an electrostatic offset voltage is a proxy for stiffness changes caused by acceleration, magnetic field, or temperature, the same scheme should transfer to real physical sensing axes.
  • The presence of hysteresis gives the resonator the short-term memory needed for reservoir computing, making in-physical-sensor computation a plausible next step.
  • Arranged in an array, such resonators could multiply a vector of physical inputs by programmable sensitivity values, including negative and zero entries, in analogy to a memristor crossbar.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, a direct test of the cross-domain claim would be to couple the same parametrically modulated resonator to a genuine acceleration or magnetic-field input and check that the same pinched loops survive without the electrical offset-voltage proxy.
  • The phase-crossing explanation predicts that the pinched crossing point coincides with the stiffness where the open-loop phase response changes from three intersections with the set-point to one; a precomputed open-loop phase map over stiffness would test this.
  • The mechanism is generic to any weakly coupled oscillator under phase tracking, so similar pinched hysteresis may appear in photonic, phononic, or macroscopic mechanical systems, not only in MEMS.
  • The memory demonstrated here is volatile and depends on the phase-locked loop staying active; a non-volatile version would require a state variable that persists after the loop is turned off, which this paper does not claim.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper reports the observation of pinched hysteresis loops in a commercial SOIMUMPs DETF MEMS resonator operated under parametric modulation. The authors apply one, two, and three parametric modulation signals to virtually couple multiple modes and, using a closed-loop PLL to track a fixed phase set-point while a 10 mHz offset voltage perturbs the stiffness, record frequency and amplitude responses that show a single hysteresis loop and multiple pinched loops. They support the interpretation with a harmonic-balance simulation of the one-PMS case and with open-loop phase-crossing measurements at three offset voltages. The paper introduces the term 'MemReSensor' and argues that the observed memory and multi-sensitivity suggest applications in in-sensor computing and cross-domain matrix multiplication.

Significance. If the pinched hysteresis is confirmed to be an intrinsic property of the resonator rather than an artifact of the measurement loop, this is a valuable first demonstration of memristor-like hysteresis in a micromechanical resonator. The systematic exploration of one, two, and three PMSs is a strength, and the experimental figures clearly show branch crossings and discontinuities. The HBM simulation, while qualitative and parameterized from experiment, gives a useful consistency check. However, the central claim is not yet fully supported: the closed-loop nature of the measurements and the unaddressed origin condition of the memristor fingerprint leave two load-bearing questions open. With the additional control experiments and clarifications proposed below, the paper could become a solid contribution.

major comments (4)
  1. [Section III-B, IV-A-1] The quasi-static assumption used to interpret the closed-loop measurements is not established. Stating that the 10 mHz voffset sweep is 'well below the PLL bandwidth of 20 Hz' is insufficient because the PLL bandwidth is a linear small-signal quantity and the loop dynamics near the phase-crossing degeneracies (the very points that trigger the observed jumps) can differ substantially. The open-loop data in Fig. 6 are provided for only three voffset values and therefore cannot reconstruct the branch structure over the full sweep. Please supply either (i) continuous open-loop phase/frequency responses as a function of voffset across the entire range, (ii) a sweep-rate dependence study (e.g., 1 mHz, 10 mHz, 50 mHz), or (iii) a closed-loop simulation that includes the PLL/PID controller, in order to show that the hysteresis is intrinsic to the resonator and not an artifact of the tracking loop.
  2. [Section IV-A-4, Figs. 8b, 10b, 12b] The claim that the loops satisfy the memristor pinched-hysteresis fingerprint of [5] is not justified. In a memristor the pinched loop passes through the origin of the input-output plane; here the crossings occur at v_offset = 0 V but at nonzero output frequency or amplitude. Please replot the responses in terms of a relative output (e.g., frequency deviation from the unperturbed f1 and amplitude relative to its value at v_offset = 0) so that the loop passes through the origin, or explicitly define the generalized pinched-hysteresis criterion being used and state how it connects to [5].
  3. [Introduction, Section V] The abstract and conclusion describe the demonstration as 'cross-domain' with a physical input and electrical output, but the input in all experiments is an electrical offset voltage used only as a proxy for stiffness. This limitation is acknowledged in the Introduction, but the conclusion restates the cross-domain claim without qualification. Please either temper the cross-domain wording in the abstract and conclusion or present at least one demonstration with a physical stimulus (or a physically modulated stiffness) to support the generalization.
  4. [Section IV-A, Figs. 6 and 8] The open-loop phase-crossing analysis in Fig. 6, which is used to explain the hysteresis mechanism, does not state the PMS parameters used. If those parameters differ from the v1,3_p = 4 V, Δf1,3_p = 0 Hz used in the closed-loop data of Fig. 8, the degeneracy points at approximately ±0.3 V in Fig. 6 cannot be directly compared with the discontinuities observed at approximately +1 V and -0.75 V in Fig. 8a. Please state the parameters for Fig. 6 and reconcile this discrepancy.
minor comments (5)
  1. [Table I] The quality-factor column labels 'Q1' and 'Q2' should be 'Q1' and 'Q3' to match the first and third modes used in the simulation.
  2. [Section III-B] The sentence 'The PMSs used have frequencies determined as follows, and Δf1,jp ...' is awkward; please rephrase.
  3. [Section IV-A-1] The phrase 'This sweeping process is again comparable' uses 'again' without a prior comparison; consider removing it.
  4. [Section IV-B-2] The sentence 'the phenomenon that satisfies the definition of pinched hysteresis [5] occurs, even though a pinched intermediate branch, rather than a crossing, exists' is self-contradictory in its use of 'pinched'; clarify whether the branch overlap is intended to count as a pinch.
  5. [Section III-B] Please specify the PLL/PID controller settings (e.g., proportional/integral gains and the method used to determine the 20 Hz bandwidth) so that the quasi-static claim can be reproduced.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the central result is an experimental observation, and the simulation is illustrative rather than an independent prediction.

full rationale

The paper's central claim is an observed pinched hysteresis in a MEMS resonator under closed-loop PLL tracking of a phase set-point. This is an experimental finding, not a derivation from an input that already contains the result. The theory in Sec. II-A is a generalized equation of motion for parametrically modulated coupled modes, taken from prior work [18], [19], [29]; the paper does not claim to derive the pinched hysteresis from first principles, and the observation does not reduce to those equations. The HBM simulation in Sec. II-B uses parameters 'extracted based on a combination of experimental data and numerical estimations' (Tab. I), and Sec. IV-A-3 says the measured loop 'agree[s] well with the simulation.' That agreement is illustrative, not an independent prediction, because the model parameters are partly informed by the same device; however, this is a modeling limitation, not a circular derivation, and it does not affect the reported observation itself. Several references are self-citations (e.g., [18], [19], [29]), but they supply the parametric-modulation framework and are not invoked as a uniqueness theorem or as the sole justification for the existence of pinched hysteresis; the existence claim rests on the measured loops in Figs. 8, 10, and 12. The quasi-static PLL assumption and the voffset-as-stiffness-proxy premise are correctness and validity concerns external to circularity. No step in the paper reduces by construction to its own inputs.

Assumptions & free parameters 3 free parameters · 6 assumptions · 0 invented entities

The experimental observation of pinched hysteresis is self-contained and does not rely on the simulation, but the one-PMS harmonic balance simulation uses many modal and coupling parameters chosen from a mix of experiment and numerical estimates, not independently measured values. The conversion from offset voltage to stiffness uses a geometric coefficient from prior work. No new physical entities are introduced; MemReSensor is a proposed name rather than a new dynamic variable.

free parameters (3)
  • Effective modal masses and damping coefficients in Table I = m1 = 2.17e-10 kg, m3 = 9.06e-10 kg, c1 = 9.16e-8 Ns/m, c3 = 2.12e-7 Ns/m
    Chosen from a mix of experimental data and numerical estimates to represent the device in the one-PMS harmonic balance simulation. They affect the simulated hysteresis shape but are not independently measured in this paper.
  • Pumping coefficients lambda_p and Gamma_p for the one-PMS simulation = lambda_13^p = 0.38, lambda_31^p = 0.42, Gamma_13^p = -0.33, Gamma_31^p = -0.33
    Effective parametric coupling strengths used in Eq. 1 to reproduce the measured response; treated as representative values rather than independently derived constants.
  • Electrostatic mode-shape coefficient phi_1 = 0.197
    Taken from reference [29] to convert bias voltage to a stiffness change of roughly 0.126 N/m/V. This is a modeling input used to compare theory with experiment, not fit in this paper.
assumptions (6)
  • domain assumption The generalized equations of motion in Eq. 1 describe the slotted DETF resonator under multiple parametric modulation signals.
    The equations are taken from prior work [18], [29] and assumed valid for the device used here. Invoked in Sec. II-A.
  • domain assumption Red-detuned parametric modulation creates virtual coupling between mechanical modes and enables mode switching.
    Assumed mechanism from [18], [28] and used throughout the interpretation of the hysteresis branches.
  • domain assumption An electrical offset voltage change is equivalent to a stiffness perturbation caused by physical inputs such as acceleration or magnetic field.
    This equivalence is central to the cross-domain MemReSensor claim. It is asserted in Sec. I and Sec. II-A using references [21], [22], not demonstrated with a physical input in this paper.
  • domain assumption The 10 mHz sinusoidal sweep with a PLL bandwidth of 20 Hz represents a quasi-static response.
    Stated in Sec. III-B. If this assumption fails, the hysteresis could be influenced by PLL dynamics rather than by resonator bistability.
  • domain assumption PLL tracking of the 90 degree phase set-point follows the phase-crossing branches of the coupled-mode system.
    Used in Sec. IV-A-1 to map phase-crossing degeneracies to frequency and amplitude discontinuities.
  • domain assumption Duffing nonlinearity is negligible in the experiments.
    The authors state that drive amplitudes remain below the linear limit with no obvious Duffing nonlinearity, in Sec. III-B. This matters because Duffing nonlinearity also produces hysteresis and could confound the pinched hysteresis interpretation.

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Cite this review

Pith. "Pith review of Toward Memristor-like Resonant Sensors: Observation of Pinched Hysteresis within MEMS Resonators." pith.science (2026). https://pith.science/paper/CBUZ53BX

@misc{pith2026250601749,
  author       = {Pith},
  title        = {Pith review of: Toward Memristor-like Resonant Sensors: Observation of Pinched Hysteresis within MEMS Resonators},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CBUZ53BX}},
  note         = {Machine review of arXiv:2506.01749}
}
read the original abstract

Memristors, uniquely characterized by their pinched hysteresis loop fingerprints, have attracted significant research interest over the past decade, due to their enormous potential for novel computation and artificial intelligence applications. Memristors are widely regarded as the fourth fundamental electrical component, with voltage and current being their input and output signals. In broader terms, similar pinched hysteresis behavior should also exist in other physical systems across domains (e.g., physical input and electrical output), hence linking the real physical world with the digital domain (e.g., in the form of a physical sensor). In this work, we report the first observation of pinched hysteresis behavior in a micro-electro-mechanical systems (MEMS) resonator device, showing that it is viable to create resonant MEMS sensors incorporating memristor-like properties, i.e., MemReSensor. We envisage that this will lay the foundations for a new way of fusing MEMS with artificial intelligence (AI), such as creating in-physical-sensor computing, as well as in-sensor AI, e.g., multi-mode in-sensor matrix multiplication across domains.

Figures

Figures reproduced from arXiv: 2506.01749 by the authors.

Figure 1
Figure 1. Illustration of virtual coupling when using one PMS. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The simulated (a) frequency - single hysteresis loop, [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. A block diagram of the experimental setup used for characterization of the DUT, with an optical image of the DUT [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figures from the paper (8 more)
Figure 6
Figure 6. Figure 6: The open-loop (black) and closed-loop (orange) fre [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 5
Figure 5. Figure 5: The (a) sinusoidal/quasi-static (10 mHz) [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 7
Figure 7. Figure 7: The output amplitude and phase responses when [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: The (a) frequency response - single hysteresis loop, [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]
Figure 9
Figure 9. Figure 9: The output amplitude and phase responses when [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]
Figure 10
Figure 10. Figure 10: The (a) frequency response - pinched hysteresis and (b) amplitude response - two distinct pinched hysteresis loops, when sweeping the offset (sinusoidal wave, 10 mHz) for v 1,2 p = v 1,3 p = 3 V, ∆f 1,2 p = 0 Hz and ∆f 1,3 p = 100 Hz. though a pinched intermediate bra…
Figure 12
Figure 12. Figure 12: The (a) frequency response - pinched hysteresis with two pinched intermediate branches, and (b) amplitude response - three distinct pinched hysteresis loops with their own pinched crossings, when sweeping the offset (sinusoidal wave, 10 mHz) for v 1,2 p = v 1,3 p = v …
Figure 11
Figure 11. Figure 11: The output amplitude and phase responses when [PITH_FULL_IMAGE:figures/full_fig_p008_11.png]

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Works this paper leans on

45 extracted references · 44 canonical work pages

  1. [5]

    If it’s pinched it’s a memristor,

    L. Chua, “If it’s pinched it’s a memristor,” Semiconductor Science and Technology, vol. 29, no. 10, p. 104001, 2014

    work page 2014

  2. [1]

    Memristor-the missing circuit element,

    L. Chua, “Memristor-the missing circuit element,” IEEE Transactions on circuit theory , vol. 18, no. 5, pp. 507–519, 1971

    1971

  3. [2]

    The missing memristor found,

    D. B. Strukov, G. S. Snider, D. R. Stewart, and R. S. Williams, “The missing memristor found,” Nature, vol. 453, no. 7191, pp. 80–83, 2008

    work page 2008

  4. [3]

    Brain-inspired computing with memristors: Challenges in devices, circuits, and systems,

    Y . Zhang, Z. Wang, J. Zhu, Y . Yang, M. Rao, W. Song, Y . Zhuo, X. Zhang, M. Cui, L. Shen et al. , “Brain-inspired computing with memristors: Challenges in devices, circuits, and systems,” Applied Physics Reviews, vol. 7, no. 1, 2020

    work page 2020

  5. [4]

    Memristor-based hardware accelerators for artificial intelligence,

    Y . Huang, T. Ando, A. Sebastian, M.-F. Chang, J. J. Yang, and Q. Xia, “Memristor-based hardware accelerators for artificial intelligence,” Na- ture Reviews Electrical Engineering , pp. 1–14, 2024

    work page 2024

  6. [6]

    What are memristor, memcapacitor, and meminductor?

    Z. Yin, H. Tian, G. Chen, and L. O. Chua, “What are memristor, memcapacitor, and meminductor?” IEEE Transactions on Circuits and Systems II: Express Briefs , vol. 62, no. 4, pp. 402–406, 2015

    work page 2015

  7. [7]

    Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide,

    V . K. Sangwan, H.-S. Lee, H. Bergeron, I. Balla, M. E. Beck, K.- S. Chen, and M. C. Hersam, “Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide,” Nature, vol. 554, no. 7693, pp. 500–504, 2018

    work page 2018

  8. [8]

    Energy-efficient memca- pacitor devices for neuromorphic computing,

    K.-U. Demasius, A. Kirschen, and S. Parkin, “Energy-efficient memca- pacitor devices for neuromorphic computing,” Nature Electronics, vol. 4, no. 10, pp. 748–756, 2021

    work page 2021

Show all 45 references
  1. [9]

    Beyond memristors: Neuromorphic computing using meminductors,

    F. Z. Wang, “Beyond memristors: Neuromorphic computing using meminductors,” Micromachines, vol. 14, no. 2, p. 486, 2023

    2023

  2. [10]

    Progress and challenges for memtransistors in neuromorphic circuits and systems,

    X. Yan, J. H. Qian, V . K. Sangwan, and M. C. Hersam, “Progress and challenges for memtransistors in neuromorphic circuits and systems,” Advanced Materials, vol. 34, no. 48, p. 2108025, 2022

    2022

  3. [11]

    Ultraviolet mem-sensors: Flexible anisotropic composites featuring giant photocurrent enhancement,

    A. Chiolerio, I. Roppolo, V . Cauda, M. Crepaldi, S. Bocchini, K. Bejtka, A. Verna, and C. Pirri, “Ultraviolet mem-sensors: Flexible anisotropic composites featuring giant photocurrent enhancement,” Nano Research, vol. 8, pp. 1956–1963, 2015

    1956

  4. [12]

    Concept and modelling of memsensors as two termi- nal devices with enhanced capabilities in neuromorphic engineering,

    A. Vahl, J. Carstensen, S. Kaps, O. Lupan, T. Strunskus, R. Adelung, and F. Faupel, “Concept and modelling of memsensors as two termi- nal devices with enhanced capabilities in neuromorphic engineering,” Scientific Reports, vol. 9, no. 1, p. 4361, 2019. 10

    2019

  5. [13]

    A resonant lorentz-force magnetometer exploiting blue sideband actuation to enhance sensitivity and resolution,

    Y . Wang, X. Song, J. Xi, F. Li, L. Xu, H. Liu, C. Wang, S. Kuang, L.-C. Tu, M. Kraft et al., “A resonant lorentz-force magnetometer exploiting blue sideband actuation to enhance sensitivity and resolution,” Journal of Microelectromechanical Systems, vol. 31, no. 3, pp. 402–407, 2022

    2022

  6. [14]

    A high resolution differential mode-localized MEMS accelerometer,

    M. Pandit, C. Zhao, G. Sobreviela, X. Zou, and A. Seshia, “A high resolution differential mode-localized MEMS accelerometer,” Journal of Microelectromechanical Systems, vol. 28, no. 5, pp. 782–789, 2019

    2019

  7. [15]

    An fbar circulator,

    M. M. Torunbalci, T. J. Odelberg, S. Sridaran, R. C. Ruby, and S. A. Bhave, “An fbar circulator,” IEEE Microwave and Wireless Components Letters, vol. 28, no. 5, pp. 395–397, 2018

    2018

  8. [16]

    Highly-linear magnet-free microelec- tromechanical circulators,

    Y . Yu, G. Michetti, A. Kord, M. Pirro, D. L. Sounas, Z. Xiao, C. Cas- sella, A. Al `u, and M. Rinaldi, “Highly-linear magnet-free microelec- tromechanical circulators,” Journal of Microelectromechanical Systems , vol. 28, no. 6, pp. 933–940, 2019

    2019

  9. [17]

    Non-reciprocal transmission of microwave acoustic waves in nonlinear parity–time symmetric resonators,

    L. Shao, W. Mao, S. Maity, N. Sinclair, Y . Hu, L. Yang, and M. Lon ˇcar, “Non-reciprocal transmission of microwave acoustic waves in nonlinear parity–time symmetric resonators,” Nature Electronics, vol. 3, no. 5, pp. 267–272, 2020

    2020

  10. [18]

    Dynamic modulation of modal coupling in microelectromechanical gyroscopic ring resonators,

    X. Zhou, C. Zhao, D. Xiao, J. Sun, G. Sobreviela, D. D. Gerrard, Y . Chen, I. Flader, T. W. Kenny, X. Wuet al., “Dynamic modulation of modal coupling in microelectromechanical gyroscopic ring resonators,” Nature communications, vol. 10, no. 1, p. 4980, 2019

    2019

  11. [19]

    Toward high-resolution inertial sensors employing paramet- ric modulation in coupled micromechanical resonators,

    C. Zhao, X. Zhou, M. Pandit, G. Sobreviela, S. Du, X. Zou, and A. Seshia, “Toward high-resolution inertial sensors employing paramet- ric modulation in coupled micromechanical resonators,” Physical Review Applied, vol. 12, no. 4, p. 044005, 2019

    2019

  12. [20]

    A vibrating beam MEMS accelerometer for gravity and seismic measurements,

    A. Mustafazade, M. Pandit, C. Zhao, G. Sobreviela, Z. Du, P. Steinmann, X. Zou, R. T. Howe, and A. A. Seshia, “A vibrating beam MEMS accelerometer for gravity and seismic measurements,” Scientific reports, vol. 10, no. 1, p. 10415, 2020

    2020

  13. [21]

    En- hancing parametric sensitivity in electrically coupled MEMS resonators,

    P. Thiruvenkatanathan, J. Yan, J. Woodhouse, and A. A. Seshia, “En- hancing parametric sensitivity in electrically coupled MEMS resonators,” Journal of Microelectromechanical Systems , vol. 18, no. 5, pp. 1077– 1086, 2009

    2009

  14. [22]

    A review on in situ stiffness adjustment methods in MEMS,

    M. De Laat, H. P. Garza, J. Herder, and M. Ghatkesar, “A review on in situ stiffness adjustment methods in MEMS,”Journal of Micromechanics and Microengineering, vol. 26, no. 6, p. 063001, 2016

    2016

  15. [23]

    Near-sensor and in-sensor computing,

    F. Zhou and Y . Chai, “Near-sensor and in-sensor computing,” Nature Electronics, vol. 3, no. 11, pp. 664–671, 2020

    2020

  16. [24]

    Hysteretic reservoir,

    C. Caremel, Y . Kawahara, and K. Nakajima, “Hysteretic reservoir,” Physical Review Applied , vol. 22, no. 6, p. 064045, 2024

    2024

  17. [25]

    A closed-loop system for resonant MEMS sensors subject to blue-sideband excitation,

    L. Xu, J. Xi, L. Gao, F. Li, J. Pi, C. Li, K. Wang, X. Xiong, Y . Wang, H. Liu et al. , “A closed-loop system for resonant MEMS sensors subject to blue-sideband excitation,” Journal of Microelectromechanical Systems, vol. 31, no. 4, pp. 690–699, 2022

    2022

  18. [26]

    Multiple parameter decoupling for resonant MEMS sensors exploiting blue sideband excitation,

    J. Xi, L. Xu, Y . Wang, H. Liu, X. Xiong, X. Zou, C. Li, F. Hu, C. Wang, M. Kraft et al. , “Multiple parameter decoupling for resonant MEMS sensors exploiting blue sideband excitation,” Journal of Microelectrome- chanical Systems, vol. 32, no. 5, pp. 426–436, 2023

    2023

  19. [27]

    PT1. 41-On the tunability of resonant MEMS sensor subject to blue sideband excitation,

    E. Uka, C. Zhao, C. Lin, and J. Chen, “PT1. 41-On the tunability of resonant MEMS sensor subject to blue sideband excitation,” in EUROSENSORS XXXVI, Debrecen, Hungary, 2024, pp. 225–226, doi: 10.5162/EUROSENSORSXXXVI/PT1.41

    2024 doi

  20. [28]

    Amplitude-modulated resonant accelerometer employing parametric pump,

    H. Zhang, D. Chen, M. Pandit, J. Sun, C. Zhao, and A. Seshia, “Amplitude-modulated resonant accelerometer employing parametric pump,” Applied Physics Letters , vol. 117, no. 16, 2020

    2020

  21. [29]

    On enhancing the sensitivity of resonant thermometers based on parametric modulation,

    C. Li, J. Xi, Y . Wang, F. Li, L. Gao, H. Liu, C. Zhao, and L.-C. Tu, “On enhancing the sensitivity of resonant thermometers based on parametric modulation,” Journal of Microelectromechanical Systems, vol. 30, no. 4, pp. 539–549, 2021

    2021

  22. [30]

    Computational and quasi-analytical models for non-linear vibrations of resonant MEMS and NEMS sensors,

    N. Kacem, S. Baguet, S. Hentz, and R. Dufour, “Computational and quasi-analytical models for non-linear vibrations of resonant MEMS and NEMS sensors,” International journal of non-linear mechanics , vol. 46, no. 3, pp. 532–542, 2011

    2011

  23. [31]

    Engineering MEMS resonators with low thermoelastic damping,

    A. Duwel, R. N. Candler, T. W. Kenny, and M. Varghese, “Engineering MEMS resonators with low thermoelastic damping,” Journal of micro- electromechanical systems, vol. 15, no. 6, pp. 1437–1445, 2006

    2006

  24. [32]

    Analysis of fre- quency stability and thermoelastic effects for slotted tuning fork MEMS resonators,

    V . Zega, A. Frangi, A. Guercilena, and G. Gattere, “Analysis of fre- quency stability and thermoelastic effects for slotted tuning fork MEMS resonators,” Sensors, vol. 18, no. 7, p. 2157, 2018

    2018

  25. [33]

    A review of actuation and sensing mechanisms in MEMS-based sensor devices,

    A. S. Algamili, M. H. M. Khir, J. O. Dennis, A. Y . Ahmed, S. S. Alabsi, S. S. Ba Hashwan, and M. M. Junaid, “A review of actuation and sensing mechanisms in MEMS-based sensor devices,” Nanoscale research letters, vol. 16, pp. 1–21, 2021

    2021

  26. [34]

    Thermoelastic damping in micro-and nanomechanical systems,

    R. Lifshitz and M. L. Roukes, “Thermoelastic damping in micro-and nanomechanical systems,” Physical review B , vol. 61, no. 8, p. 5600, 2000

    2000

  27. [35]

    Temperature dependence of the elastic constants of doped silicon,

    E. J. Ng, V . A. Hong, Y . Yang, C. H. Ahn, C. L. Everhart, and T. W. Kenny, “Temperature dependence of the elastic constants of doped silicon,” Journal of microelectromechanical systems , vol. 24, no. 3, pp. 730–741, 2014

    2014

  28. [36]

    Higher-order singularities in phase-tracked electromechanical oscillators,

    X. Zhou, X. Ren, D. Xiao, J. Zhang, R. Huang, Z. Li, X. Sun, X. Wu, C.-W. Qiu, F. Nori et al. , “Higher-order singularities in phase-tracked electromechanical oscillators,” Nature Communications, vol. 14, no. 1, p. 7944, 2023

    2023

  29. [37]

    Nonlinear dynamics of spring softening and hardening in folded-MEMS comb drive resonators,

    A. M. Elshurafa, K. Khirallah, H. H. Tawfik, A. Emira, A. K. A. Aziz, and S. M. Sedky, “Nonlinear dynamics of spring softening and hardening in folded-MEMS comb drive resonators,” Journal of Microelectromechanical Systems, vol. 20, no. 4, pp. 943–958, 2011

    2011

  30. [38]

    Resistance switching memories are memristors,

    L. Chua, “Resistance switching memories are memristors,” Handbook of memristor networks , pp. 197–230, 2019

    2019

  31. [39]

    A review on coupled MEMS resonators for sensing applications utilizing mode localization,

    C. Zhao, M. H. Montaseri, G. S. Wood, S. H. Pu, A. A. Seshia, and M. Kraft, “A review on coupled MEMS resonators for sensing applications utilizing mode localization,” Sensors and Actuators A: Physical, vol. 249, pp. 93–111, 2016

    2016

  32. [40]

    Memristors—from in-memory computing, deep learning acceleration, and spiking neural networks to the future of neuromorphic and bio-inspired computing,

    A. Mehonic, A. Sebastian, B. Rajendran, O. Simeone, E. Vasilaki, and A. J. Kenyon, “Memristors—from in-memory computing, deep learning acceleration, and spiking neural networks to the future of neuromorphic and bio-inspired computing,” Advanced Intelligent Systems , vol. 2, no...

    2020

  33. [41]

    MEMS reservoir computing system with stiffness modulation for multi-scene data pro- cessing at the edge,

    X. Guo, W. Yang, X. Xiong, Z. Wang, and X. Zou, “MEMS reservoir computing system with stiffness modulation for multi-scene data pro- cessing at the edge,” Microsystems & Nanoengineering , vol. 10, no. 1, p. 84, 2024

    2024

  34. [42]

    Sparse matrix multiplication in a record-low power self- rectifying memristor array for scientific computing,

    J. Li, S.-g. Ren, Y . Li, L. Yang, Y . Yu, R. Ni, H. Zhou, H. Bao, Y . He, J. Chen et al., “Sparse matrix multiplication in a record-low power self- rectifying memristor array for scientific computing,” Science Advances, vol. 9, no. 25, p. eadf7474, 2023

    2023

  35. [43]

    STDP and STDP variations with memristors for spiking neuromorphic learning systems,

    T. Serrano-Gotarredona, T. Masquelier, T. Prodromakis, G. Indiveri, and B. Linares-Barranco, “STDP and STDP variations with memristors for spiking neuromorphic learning systems,” Frontiers in neuroscience, vol. 7, p. 2, 2013

    2013

  36. [44]

    An artificial nociceptor based on a diffusive memristor,

    J. H. Yoon, Z. Wang, K. M. Kim, H. Wu, V . Ravichandran, Q. Xia, C. S. Hwang, and J. J. Yang, “An artificial nociceptor based on a diffusive memristor,” Nature communications, vol. 9, no. 1, p. 417, 2018

    2018

  37. [45]

    A three degree-of-freedom weakly coupled resonator sensor with enhanced stiff- ness sensitivity,

    C. Zhao, G. S. Wood, J. Xie, H. Chang, S. H. Pu, and M. Kraft, “A three degree-of-freedom weakly coupled resonator sensor with enhanced stiff- ness sensitivity,” Journal of Microelectromechanical Systems , vol. 25, no. 1, pp. 38–51, 2015. 11 BIOGRAPHY Erion Uka received the M....

    2015

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.