Improved Detection of Magnetic Signals by a MEMS Sensor Using Stochastic Resonance Agustı´n L. Herrera-May 1 , Jesus A. Tapia 3 , Sau´ l M. Domı´nguez-Nicola´ s 1,5 , Raul Juarez-Aguirre 1 , Edmundo A. Gutierrez-D 4 , Amira Flores 2 , Eduard Figueras 6 , Elias Manjarrez 2 * 1Micro and Nanotechnology Research Center, Universidad Veracruzana, Boca del Rı ´o, Veracruz, Me´ xico, 2Institute of Physiology, Beneme´ rita Universidad Auto´ noma de Puebla, Puebla, Puebla, Me
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  Improved Detection of Magnetic Signals by a MEMSSensor Using Stochastic Resonance Agustı´ n L. Herrera-May 1 , Jesus A. Tapia 3 , Sau´ l M. Domı´ nguez-Nicola´ s 1,5 , Raul Juarez-Aguirre 1 ,Edmundo A. Gutierrez-D 4 , Amira Flores 2 , Eduard Figueras 6 , Elias Manjarrez 2 * 1 Micro and Nanotechnology Research Center, Universidad Veracruzana, Boca del Rı´o, Veracruz, Me´xico,  2 Institute of Physiology, Beneme´rita Universidad Auto´noma dePuebla, Puebla, Puebla, Me´xico,  3 School of Biology, Beneme´rita Universidad Auto´noma de Puebla, Puebla, Puebla, Me´xico,  4 Department of Electronics, Instituto Nacionalde Astrofı´sica O´ptica y Electro´nica, INAOE, Puebla, Puebla, Me´xico,  5 Department of Automatic Control, CINVESTAV-IPN, Mexico City, Distrito Federal, Me´xico, 6 Microelectronics Institute of Barcelona, IMB-CNM, CSIC, Bellaterra, Barcelona, Spain Abstract We introduce the behavior of the electrical output response of a magnetic field sensor based on microelectromechanicalsystems (MEMS) technology under different levels of controlled magnetic noise. We explored whether a particular level of magnetic noise applied on the vicinity of the MEMS sensor can improve the detection of subthreshold magnetic fields. Weexamined the increase in the signal-to-noise ratio (SNR) of such detected magnetic fields as a function of the magnetic noiseintensity. The data disclosed an inverted U-like graph between the SNR and the applied magnetic noise. This finding showsthat the application of an intermediate level of noise in the environment of a MEMS magnetic field sensor improves itsdetection capability of subthreshold signals via the stochastic resonance phenomenon. Citation:  Herrera-May AL, Tapia JA, Domı´nguez-Nicola´s SM, Juarez-Aguirre R, Gutierrez-D EA, et al. (2014) Improved Detection of Magnetic Signals by a MEMSSensor Using Stochastic Resonance. PLoS ONE 9(10): e109534. doi:10.1371/journal.pone.0109534 Editor:  Adam R. Hall, Wake Forest University School of Medicine, United States of America Received  May 2, 2014;  Accepted  September 11, 2014;  Published  October 15, 2014 Copyright:   2014 Herrera-May et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the srcinal author and source are credited. Data Availability:  The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper. Funding:  This work was supported by the grants PIFI-VIEP-CONACYT 153583 (EM), Ca´tedra Marcos Moshinsky (EM), Cuerpos Acade´micos PROMEP BUAP (AF, EM),Vicerrectorı´a de Docencia BUAP (AF, EM), Vicerrectorı´a de Investigacio´n y de Estudios de Posgrado VIEP BUAP (AF, EM), PROMEP 4543 EXB 468 (AHM) andPROMEP-Red: ‘‘Instrumentacio´n-de-Sensores-para-Aplicaciones-de-Fisiologı´a-y-Biomedicina’’ (AHM, EAGD, AF, EM) Me´xico. The funders had no role in studydesign, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests:  The authors have declared that no competing interests exist.* Email: eliasmanjarrez@gmail.com Introduction Stochastic resonance is a phenomenon of nonlinear systemscharacterized by a response increase of the system induced by aparticular level of input noise. The essential feature of thisphenomenon is that the SNR versus input noise is an inverted U-like function characterized by maximal enhancement of SNR at aspecific noise intensity value.In biology, Douglass et al. [1] published the first description of stochastic resonance (SR) in crayfish mechanoreceptors. Furtherstudies involved the analysis of SR in other sensory receptors fortactile, vestibular, auditory and visual modalities [2–16]. Thesebiological studies sparked alternative theoretical approaches andthe development of new sensors that employ noise to improve theirdetection capability [17–24]. For instance, arrays of MEMS flowsensors [24] were inspired by the acoustic flow-sensitive hairs of the cricket’s cerci. Although there are diverse biological and artificial sensorsemploying noise to improve signal detection, there are not yetfabricated sensors that include magnetic noise to detect weak magnetic fields. Moreover, there is little information about thephenomenon of stochastic resonance associated to magnetic fields.The first formal description of such phenomenon was introducedby Grigorenko et al., [25–26], who proposed a method formagnetic field measurement in nanometer scale. This method isbased upon stochastic resonance in arrays of magnetic nanopar-ticles. The magnetostochastic resonance was proposed for studying magnetization fluctuations in a ferromagnet, in particular, forobserving a macroscopic quantum tunneling of the magneticmoment [25]. Subsequently, in 1995 Hibbs et al. [27] described anexperiment in which the magnetic signal detected by a radiofrequency superconducting quantum interference device (RFSQUID) sensor was enhanced by the addition of an optimal levelof noise into the device. However, these studies did not report thedevelopment of a magnetic field sensor including a magnetic noisegenerator to detect weak magnetic fields. The purpose of thepresent study is to introduce the first MEMS magnetic field sensorthat can improve the detection of subthreshold magnetic fieldsignals by applying magnetic noise. In this case, the magnetic noiseis injected in the vicinity of a simpler sensor based onmicroelectromechanical systems (MEMS) technology.We employed the same MEMS sensor as in our previous studies[28–37,39,40–41]. The MEMS sensors have potential applicationsin automotive industry, military instruments, telecommunications,and the biomedical sector [38–41]. MEMS sensors have importantadvantages such as a small size, a lightweight, low-powerconsumption, and a high resolution [38]. Most of these sensorsmake use of the Lorentz force to detect a magnetic field throughthe use of different sensing techniques, including the capacitive,the optical, or the piezoresistive. The importance of the presentstudy is that these sensors could improve their detection PLOS ONE | www.plosone.org 1 October 2014 | Volume 9 | Issue 10 | e109534  capabilities for subthreshold magnetic fields using an intermediatelevel of magnetic noise. Materials and Methods MEMS magnetic field sensor The MEMS magnetic field sensor makes use of the Lorentzforce based on a piezoresistive sensing technique. This sensorincludes a resonant silicon structure (700  m m 6 600  m m 6 5  m m), analuminum loop (1  m m thickness) and a Wheatstone bridge withfour type-p piezoresistors, as shown in Figure 1. It has beendeveloped by the MEMS group from the Micro and Nanotech-nology Research Center (MICRONA) of the UniversidadVeracruzana with collaboration of the Microelectronics Instituteof Barcelona (IMB-CNM, CSIC) [13]. Its resonant structureconsists of four bending silicon beams and an arrangement of longitudinal and transversal silicon beams. This resonant structureis connected to a silicon substrate by means of two support beams(60 6 40 6 5  m m). In addition, two piezoresistors are placed on thesurface of the silicon substrate and other two piezoresistors arelocated on two bending beams. A sinusoidal electrical current is applied through the aluminumloop of the MEMS sensor to interact with an external magneticflux density parallel to the length of the resonant structure. Thisinteraction generates a Lorentz force on the structure, whichcauses an oscillation motion (see Figure 2). Then it is amplifiedwhen the frequency of the electrical current is equal to the firstbending resonant frequency of the MEMS sensor structure. Due tothis amplified motion, the piezoresistors located on two bending beams are subjected to a longitudinal strain that changes theirinitial no-strain resistances. It produces a change in the output voltage of the Wheatstone bridge. Thus, the electrical signal of theMEMS sensor is related to the applied magnetic flux density. Design of the signal conditioning system and virtualinstrument We designed a signal conditioning system implemented on aPCB for the MEMS sensor, which contains oscillators with high-frequency stability around  6 100 ppm at room temperature. Inorder to excite the sensor in its first resonant frequency, we madean algorithm in a digital signal controller dsPIC30F4013(Microchip Technology Inc) to assure a frequency sweep with aresolution of 1 Hz. This system allows the approximately linearmeasurement of the polarity and magnitude of magnetic fluxdensity with a minimum offset. The Figure 3 shows the block diagram of the signal conditioning system for the MEMS sensor. Experimental setup and emitted magnetic field signals Pulsed and noisy magnetic fields were generated by means of two miniature solenoids with 250 loops of insulated AWG-24cooper wire. The experimental setup is shown in the Figure 4.The PCB of the MEMS sensor is energized with a dual powersupply Agilent E3631A. A Master-8 waveform generator (AMPI, Jerusalem) was used to produce the test signal, which was appliedto the first coil. This coil is placed in the region where the MEMSsensor detects the largest magnetic field. Furthermore, a secondcoil is employed to produce the magnetic noise (white Gaussiannoise, from 0 to 500 Hz) by means of a Wavetek noise generator(Model 132, San Diego, CA, USA). The typical power spectrum of this magnetic noise is illustrated in Figure 5B.The PCB output represents the MEMS sensor response in voltage mode. This signal is processed with the designed virtualinstrument. The data acquisition in voltage mode is feed throughthe PCI-DAS6031 card (Measuring Computing Corporation). Subthreshold magnetic signals We examined the effects of magnetic noise on the detectioncapability of our MEMS sensor to detect subthreshold magneticsignals. These subthreshold signals were generated by a coil andconsisted of pulsed magnetic signals of 100 ms elicited everysecond (i.e., at 1 Hz) during 120 seconds. Figure 1. SEM image of the resonant structure of the MEMSmagnetic field sensor. doi:10.1371/journal.pone.0109534.g001 Figure 2. Schematic view of the operation principle of theMEMS magnetic field sensor. doi:10.1371/journal.pone.0109534.g002Improved Magnetic Field SensorPLOS ONE | www.plosone.org 2 October 2014 | Volume 9 | Issue 10 | e109534  Data analysis Data acquisition of the magnetic signal and magnetic noise wasperformed with a sampling rate of 300 kHz (Digidata 1400 A, Axon Instruments, Molecular Devices). Spectral analysis of thedetected magnetic signals with the MEMS sensor was performed.The magnitude of the input magnetic noise was quantified bymeans of the standard deviation of the input noise. We employedthe signal-to-noise ratio (SNR) to estimate the effect of magneticnoise upon the capability of the MEMS sensor to detectsubthreshold magnetic signals. We computed the signal-to-noiseratio (SNR) for our experimental data as in previous studies aboutstochastic resonance from our laboratory [10,12].We defined the SNR as the ratio, at the input signal frequency(1 Hz), of the strength of the output power spectra peak (its area)during pulse stimulation plus noise to the output power spectraarea occurring during input noise alone. Both areas werecalculated in the frequency interval of  6 0.1 Hz around the inputsignal frequency (1 Hz). The method to calculate SNR was thefollowing: SNR ~ log 10 ð  1 : 10 : 9 S  (  f  ) df  = ð  1 : 10 : 9 N  (  f  ) df     ð 1 Þ S(f)  corresponds to the power spectrum of the periodicmagnetic signal detected with the MEMS sensor plus magneticnoise.  N(f)  is the power spectrum of the magnetic noise alonedetected with the same MEMS sensor. Statistical analysis of SNR Data were expressed as mean 6 sd. The statistical difference inSNR between zero noise and optimal noise was determined by theWilcoxon test. The comparison was considered to be significant if p , 0.05. Results We examined the effects of magnetic noise on the detectioncapability of our MEMS sensor to detect subthreshold magneticsignals. First, we obtained the input-output graph (Figure 5A,Table 1) to characterize our MEMS sensor (i.e., input is the voltage applied in the coil and output is the magnetic flux densitydetected with the MEMS sensor). Second, we also appliedsubthreshold magnetic signals to identify the threshold level of detection capability of our MEMS sensor. Third, we selected theintensity of the magnetic flux density that was just below thethreshold level of detection of our MEMS sensor (see arrow inFigure 5A). Fourth, this subthreshold signal was employed toexamine the effects of seven different levels of magnetic noise onthe detection capability of our MEMS sensor to detect thissubthreshold signal.We explored whether a particular level of magnetic noiseapplied on the vicinity of the MEMS sensor can improve thedetection of subthreshold magnetic fields. As described in methodssection we employed two coils, one to emit the magnetic signal andother to emit the magnetic noise. The MEMS sensor was placed inthe region of the maximal magnetic flux density signal emitted bya coil. Figure 3. Block diagram of the signal conditioning system of the MEMS magnetic field sensor. doi:10.1371/journal.pone.0109534.g003 Figure 4. Experimental setup for the detection of magnetic signals by a MEMS sensor using stochastic resonance. doi:10.1371/journal.pone.0109534.g004Improved Magnetic Field SensorPLOS ONE | www.plosone.org 3 October 2014 | Volume 9 | Issue 10 | e109534  We observed the stochastic resonance phenomenon in 5 of 5experiments. We found that the application of an intermediatelevel of noise in the environment of the MEMS magnetic fieldsensor improves its capability to detect subthreshold magneticsignals. Figures 6B and 6C show a magnetic signal below thethreshold and the power spectrum, respectively. Note that thepower spectrum does not exhibit a peak at the input frequency(1 Hz); i.e., the subthreshold magnetic signal was not detected byour MEMS magnetic sensor at the zero noise level. However, ourMEMS sensor was able to detect such subthreshold signal when anoptimum level of magnetic noise was applied (see Figures 6D and6E). Note the peak at 1 Hz in the power spectrum of Figure 6E.Moreover, our MEMS sensor was unable to detect the subthresh-old signal when a high level of noise was applied (Figures 6F and6G).In Figure 7 we show pooled data of the SNR versus differentlevels of noise (Table 2). We found that there is a statisticallysignificant intermediate-non-zero level of noise that improves thecapability of our MEMS sensor to detect subthreshold magnetic Figure 5. Graph employed to determine the threshold of ourMEMS sensor and a graph illustrating the typical input noise.A , input-output curve to identify the threshold level of detection of ourMEMS sensor. The horizontal axis indicates the input signal; i.e., theinput voltage applied in the coil. The vertical axis shows the outputsignal; i.e., the magnetic flux density detected with the MEMS sensor.This type of input-output curve was useful to select the appropriatedsubthreshold signal (see vertical arrow) for every stochastic resonanceexperiment.  B , the typical power spectra density (PSD) of magneticnoise (Gaussian noise from 0 to 500 Hz).doi:10.1371/journal.pone.0109534.g005     T   a     b     l   e    1  .      M     E     M     S    m    a    g    n    e    t     i    c     f     l    u    x     d    e    n    s     i    t    y    m    e    a    s    u    r    e     d    t    o    c    a     l    c    u     l    a    t    e    t     h    e     d    e    t    e    c    t     i    o    n    t     h    r    e    s     h    o     l     d    o     f    t     h    e     M     E     M     S    s    e    n    s    o    r     (     S    e    e     f     i    g    u    r    e     5     A     ) .     V      C     O     I     L      (   m    V     )    M    E    M     S    M   a   g   n   e    t     i   c    F     l   u   x    D   e   n   s     i    t   y     (       T     )    M    E    A    N     (       T     )     S    T    D     (       T     )    E    X    P    1    E    X    P    2    E    X    P    3    E    X    P    4    E    X    P    5    0  .    1    2      2     6 .     1     1     2 .     9     8 .     2     9     5 .     9    2      6     0 .     6     6  .    1    5    8  .    3    0  .    1    2      3     3 .     8     3     6 .     4     5     7 .     0     7 .     2    2      3     8 .     5     5  .    6    4    2  .    1    1  .    6      3     3 .     9    2      4     9 .     4     3     8 .     2    2      2     6 .     0     4     7 .     1     8  .    8    4    3  .    5    6  .    0    2      4     7 .     6     4     7 .     4    2      2     7 .     9     8     2 .     4    2      4     0 .     5     2  .    8    5    8  .    5    1    1  .    4    2      6     2 .     3     2     1 .     5     9     1 .     8    2      1     3     1 .     6     1     1     5 .     2     6  .    9    1    0    3  .    8    1    5  .    4      7     8 .     2     1     8     6 .     6    2      5     6 .     2     1     6     9 .     3     1     0     4 .     2     9    6  .    4    9    6  .    3    2    0  .    2      3     2     6 .     4     4     2     4 .     1     3     9     6 .     2     2     3     4 .     3     4     7     0 .     7     3    7    0  .    3    9    2  .    3    2    4  .    7      9     0     0 .     3     5     6     3 .     9     6     1     3 .     9     7     0     4 .     0     8     2     3 .     1     7    2    1  .    0    1    4    0  .    5    2    6  .    1      1     1     4     5 .     9     9     7     8 .     0     9     5     2 .     7     9     6     2 .     3     7     3     6 .     6     9    5    5  .    1    1    4    5  .    6    3    2  .    3      1     0     7     4 .     0     1     2     1     2 .     0     1     3     1     9 .     8     1     3     9     2 .     4     1     3     3     6 .     3     1    2    6    6  .    9    1    2    6  .    1    3    8  .    6      1     6     8     7 .     1     1     4     7     3 .     1     1     8     0     5 .     6     1     6     3     1 .     6     1     5     1     3 .     7     1    6    2    2  .    2    1    3    4  .    1      d    o     i   :     1     0 .     1     3     7     1     /     j    o    u    r    n    a     l .    p    o    n    e .     0     1     0     9     5     3     4 .    t     0     0     1 Improved Magnetic Field SensorPLOS ONE | www.plosone.org 4 October 2014 | Volume 9 | Issue 10 | e109534

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