Measuring and Extracting Biological Information on a new ... - CiteSeerX

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Measuring and Extracting Biological Information on a new Hand-held Biochip-based Microsystem P. A. C. Lopes, J. Germano, T. M. Almeida, L. Sousa, M. S. Piedade, F. Cardoso, H. A. Ferreira and P. P. Freitas Abstract This paper presents the techniques developed for the extraction of biological information in a recently developed hand-held biochip-based microsystem. The microsystem is based on a magneto-resistive array biochip composed of a number of sensing sites with magnetic tunneling junctions (MTJ) and diodes. To drive the MTJ, different techniques are addressed with different types of signals. Different filtering strategies are also studied, which allow the recovery of bio signals from the noise without increasing too much nor the time required to access all the sensors, nor the power consumption of the board. In conclusion, experiments with the system in a setup to detect actual bio signals are presented with encouraging results.

Index Terms biochip, microsystem, magnetic sensors, bio-molecules, signal processing.

I. Introduction One of the trends of the last decade has been the miniaturization of typical large laboratory experiments. This was made possible by the advances in microfluids and MicroElectro-Mechanical Systems (MEMS) technologies. One of the outcomes of this trend has been the so called “lab on a chip” systems [1]. For lower scale production, microsystems such P. A. C. Lopes and others are with Instituto Engenharia de Sistemas e Computadores, Investigao e Desenvolvimento (INESC-ID) and Instituto Superior Tcnico, INESC-ID, Rua Alves Redol n 9, 1000-029 Lisboa, Portugal, phone:+351213100379, email: [email protected] F. Cardoso and others are with Instituto Engenharia de Sistemas e Computadores, Microsistemas e Nanotecnologias (INESC-NM), email: [email protected]. July 7, 2006—4 : 31 pm

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as the one described in this paper offer great promise. The microsystem described in this paper [2], is based on magnetoresistive biochips. These chips have been introduced for fully integrated biomolecular recognition assays [3, 4]. In these experiments, target bio-molecules are marked with magnetic particles and are subsequently recognized by bio-molecular probes immobilized at the surface of the chip over sensing sites. The markers fringe fields are then detected by magnetic tunneling junctions (MTJs) [1]. The developed system consists of a compact, credit card size, portable hand-held microsystem for biomolecular recognition assays. II. Architecture The proposed architecture for the biochip platform is organized in two main modules (fig. 1): i) the Sensing and Processing Module (SPM) and ii) the Fluid Control and Communications Module (FCCM). The SPM integrates the biochip and provides the circuits that directly interact with the array of biosensors (biochip). The FCCM interfaces the platform with the external world by controlling the fluid carrying the magnetically tagged biomolecules and by providing wireless communication with a handheld analyzer based on a Personal Digital Assistant (PDA). Biochip Platform

SPI

RS232

Emitter/ Fluid Flux Receiver Control

Emitter/ Receiver

Bluetooth Module

Driver

Pump and Valves

Biochip

...

Switch Software Interface

Waste Container

Fluid Source

VFeed

...

Wireless

Heater control

Driver

Switch

MC USB

Sensing and Processing Module (SPM)

Control

PDA / Laptop

Fluid Control and Communication Module (FCCM)

MC/DSP Temperature Control

Current line control Magnetic Field Gen.

SPI

VSensor Current Gen. VRef

SPI

Row address

Sensor Addressing and Readout

Column address Signal Acquisition

Measure type SPI Amplified signal

Fig. 1. Full diagram of the microsystem.

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A. Current generator circuits To perform readings in the complete sensor array, the current to the sensor is generated using a Digital to Analog Converter (DAC) and a voltage-to-current converter and is multiplexed into the biochip. Figure 2 depicts the circuit diagram employed in the current generator. The presented circuit guaranties that the current that flows to the sensor Avdd=5V

Current mirror for reference sensor

SPI

Re

Re

Qm

Re

Current mirror for sensor

QSensor

QRef ISensor

Im 10-bit DAC (C)

I Ref

+

VRef

Sensor and multiplexing

QFeed I≈0

R1

Voltage to current IFeed conversion

RFeed

Fig. 2. Current generation circuit.

is the same that runs through the Operational Amplifier (OPAMP) feedback resistor, thus eliminating the temperature and the current errors introduced by the mirror and by the transistor. For the magnetic field generator, a circuit similar to the one used for the generation of the sensor drive current was used. The current intensity in the coil, and consequently the magnetic field, is controlled using the DAC, and scaled through a resistor. III. Sensing sites The biochip is composed of a number of sensing sites, formed by a MTJ and a diode. The sites are arranged in a array and accessed through line and column wires that are selected through multiplexers outside the chip. The diode has two functions: to act as a commutator that prevents access to sites (through loops), other than the selected one, and to act as a temperature sensor for biological reactions that take place on the chip. A typical set of magnetic tunnel junctions used in the biochip were characterized in [5]. The resistance of July 7, 2006—4 : 31 pm

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the MTJ varies with the transversal component of the applied magnetic field. An important characteristic of the junction is its tunneling magnetoresistance ratio, TMR. This is given by, TMR =

Rmax − Rmin Rmin

(1)

where Rmax and Rmin are, respectively, the maximum and minimum resistance values obtained with magnetic opposite saturation fields. The resistance variation follows a hysteresis curve. Assuming that the junction is being driven by a current I0 , then the sensitivity to the magnetic field of the measured voltage signal is given by, ∂v RJ v = SH = TMR(V) I0 . ∂h ∆Hmax

(2)

IV. Excitation and Acquisition In order to measure the resistance of the MTJ, a know current was applied at the site and the resulting voltage was measured. A. AC and DC measures Applying a current through the sensing site and measuring the voltage signal, will result in a signal that combines the voltage drop across the diode and across the MTJ. In order to extract the diode signal, one can simply subtract the signal measured before the insertion of the particle solution at the sensor. This was the procedure used in fig. 6. This will result is a signal that is proportional to the concentration of particles in the solution. However this is a small signal, that is embedded in a large signal. These signal levels requires the AD converter to have a large dynamic range. This problem can be reduced if the applied external magnetic field has a sinusoidal component. This alternating magnetic field will produce a corresponding variation in the resistance value. When this is multiplied by the values of the current source, it will result in an AC signal, with the same frequency July 7, 2006—4 : 31 pm

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as the one used to generate the magnetic field. This signal can then be separated from the DC voltage through a high pass filter, and then amplified. B. Selecting the excitation current value The TMR of the junction decreases with applied voltage, being maximum with zero applied voltage. For the measured set used in [5] the TMR was kept constant until about 30 mV. At this level, a TMR of about 27% was obtained. The TMR then decreases almost linearly with the applied voltage, in a range up to 500 mV. This can be modelled as, TMR(V) V =1− TMR(0) 2 V1/2

(3)

where V1/2 is about 350 mV. This suggests the use of low polarization voltages. However, a low polarization voltage implies a low drive current. The sensitivity of the measured voltage signal to the magnetic field is given by (2). For low currents, the TMR is high, but since the current is low, the sensitivity will be low. For high currents the TMR is low, making the sensitivity low. The current value that maximizes the sensitivity lies somewhere in the middle. This can be calculated using the referred equations, resulting in, I0 =

V1/2 . RJ

(4)

For the junction in [5], which has about 14.4 kΩ, the optimum current is at about 30 µA. Note that the drive current was optimized for the highest signal level. If the requirements were to optimize the signal to noise ratio, then the results would be different. However, for this application the noise level is not limited by the junction. Due to the ultra low thickness of the dielectric, the MTJ may breakdown for applied voltages over 1.1V. For example, for MTJ’s with RJ = 15.3 kΩ the maximum drive secure driving current is 65 µA. July 7, 2006—4 : 31 pm

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Noise Power spectral density

Power/Frequency (VdB/Hz)

0 −20 −40 −60 −80 −100 −120

0

0.05

0.1 0.15 Frequency (kHz)

0.2

Fig. 3. Noise power spectral density at the AD converter, with the DA configured to a maximum current of 1 mA

V. noise Measurements of noise levels in the board were made, for the case of a sinusoidal current excitation signal (AC mode). The sampling frequency was of 480 Hz. The signal was a 30 Hz, 5 µA current, injected through a 10 kΩ resistance. The noise power spectral density can be seen in fig. 3. The noise is mostly composed of four components: Harmonics of the 30 Hz frequency, quantization noise from the DA converter; 50 Hz power line frequency noise; low frequency noise; and white noise. The total noise level is about 1 mVRMS , due to DA quantization. The 50 Hz power line noise and low frequency noise amount to 370 µVRMS and the resulting noise is about 37 µVRMS . Further filtering with a 3.3s length band-pass √ filter, reduces the noise to about 8 µVRMS . The white noise floor at about −100 dB V/ Hz = √ 10uV/ Hz is mainly due to the noise figures of the DA. The high noise floor and quantization noise lead to a change of the DA scale from a maximum of 1 mA to only 100 µA. This not only reduces the quantization noise but also the √ white noise floor. The noise floor was reduced by a factor of ten, to a reasonable 1uV/ Hz, as shown in fig. 4. This figure represents the power spectral density of the noise with measurement done in AC mode, and with the amplifier set for a gain of ten. Also shown is the noise level when the load is set to a sensing site diode, where a high low-frequency noise July 7, 2006—4 : 31 pm

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is visible, which lead to an increase of the signal frequency to 325 Hz, with a sample ratio of 8 spl/periode and a conversion ratio of 2600 cvs/s. The anti-aliasing filter of the AD had a 3 dB cut frequency of 1696 Hz, and an effective noise bandwidth of 3148 Hz. In this figure there is a strong 50 Hz interference signal, but it can be removed digitally. Noise Power spectral density

Power/Frequency (V2 dB/Hz)

−40 33KOhm Resistor sensor site PIN diodo

−60 −80 −100 −120 −140

0

500 1000 frequency (Hz)

1500

Fig. 4. Noise power spectral density measured by the AD converter, with the system configured to AC mode with gain 10, and the DA configured to a maximum current of 100 µA. The peak at 325 Hz is the applied signal.

Finally fig. 5 presents the PSD of the noise at the input of the AD converter under the same conditions as in fig. 4, but measured with a spectral analyzer. The noise levels are in close agreement, and it can be seen that the simple RC anti-aliasing filter at the input of the Sigma/Delta AD converter is effective in removing the high frequency noise. Signal harmonics due to the sample-and-hold at the output of the DAC are highly visible. VI. Signal Processing In the system there are two main tasks for the signal processor embedded in the board: the generation of the excitation signal and the recovery of the biological signal. Some of the signal processing techniques rely on previously obtained [2,6] models for the MTJ and diode.

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Noise Power Spectral Density

0

RMS Frequency−1/2 (V dB Hz−1/2)

10

−2

10

−4

10

−6

10

−8

10

0

10

2

4

10 10 frequency (Hz)

6

10

Fig. 5. Signal and noise levels at the input of the AD converter, made with a spectral analyzer, with the system set to AC mode with gain 10. The chart is a superimposition of three different measurements with different measurement bandwidths. The large bandwidth of the last measurement filters out the peaks seen at lower frequencies. The peak at 325 Hz is the signal, . following peaks at signal harmonics.

A. Generating the excitation signal For AC measurements, the chosen excitation signal was sinusoidal, while for DC a continuous current was used. The sampling ratio was chosen to be a constant multiple of the sinusoidal signal. This allowed the signal to be generated very simply through a lookup table. B. Noise Filtering Assuming AC excitation, either with an AC field or an AC current, the amplitude of the voltage signal at the sensing site must be determined. This can be done using several techniques. The measured signal, y[n], can be approximated by sinusoidal signal, with analog frequency f0 , which correspond to the digital frequency w0 = 2πf0 /fA . The signal is corrupted by white noise, v[n], with standard deviation σv , y[n] = A cos(ω0 n + φ) + v[n],

(5)

and the goal is to estimate the amplitude, A, of the signal. A number of techniques are described. The DFT amplitude estimator was chosen for implementation. July 7, 2006—4 : 31 pm

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B.1 Direct RMS value calculation The first approach is simply to calculate the RMS value of the received signal, as given by (6). This produces reasonable results as long as the noise level is low. Assuming that N samples are taken from the received signal, where N is a multiple of the signal period, then the signal amplitude estimate will be, ARMS

v u u2 =t N

n0X +N −1

y[n]2 .

(6)

n=n0

This amplitude estimator is biased, its expected value is given by E[ARMS /A] = 1 + (σv /A)2 ,

(7) q

and the standard deviation, or RMS value, of the noise will be, σ(ARMS /A) =

2 σ . N (v/A)

In

our application the error will usually be dominated by the bias, since the value of N can be large. B.2 DFT amplitude estimator Since we intend to calculate the amplitude of the received signal, an obvious approach is to calculate its DFT, and determine the amplitude at the excitation frequency. However, it is not required to calculate the full DFT, only its amplitude at the given frequency. This can be calculated by,

Pn0 +N −1

2 y[n] cos(ω0 n) N Pn0 +N −1 2 y[n] sin(ω0 n) ADFT imag = n=n0 N q ADFT = A2DFT real + A2DFT real ADFT real =

n=n0

(8) (9) (10)

This is an unbiased estimator of the amplitude. Its standard deviation, for small values of the noise signal, is given by,

r σ(ADFT /A) =

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2 σ(v/A) . N

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This result is the same as the one from the RMS calculation. However, since this estimator is unbiased the resulting estimation error is usually much lower. B.3 Optimal Passband filter Assuming a model for the noise signal in (5), namely its power spectral density or its autocorrelation function, an optimal filter to remove the noise can be determined, in a form of a FIR filter [7] with impulse response wj and length N . This can be formulated as a winner filtering problem [8], where, d[n] = A cos(ω0 n + φ) in (5) is the desired signal and y[n] is the input signal. The input y[n], can be decomposed in two components, y0 [n] = A cos(ω0 n+φ) and v[n]. Only v[n] is considered stochastic. If one assumes the noise is white, as is approximately the case around the excitation frequency (section V), then the optimum filter results in the truncation of a sinusoidal signal. Defining y0 = [y0 [n], ..., y0 [n − N + 1]]T , and w = [w0 , ..., wN −1 ]T , the autocorrelation matrix of the signal is given by, R[n] = y0 y0 T + σv2 δ[i − j] and the crosscorrelation vector is P[n] = y0 y0 . This will result in a time varying optimal filter W [n]. The output of this filter is then sampled at its maximum to determine the amplitude of the sinusoid, resulting in, w=

y0 y0 [nmax ] . y0 T y0 + σv2

(12)

For the given y0 , as long as σv2 is low, and N is a multiple of the period, this results in the estimator,

Pn0 +N −1 AOpt =

n=n0

2 y[n] cos(ω0 n + φ) . N

(13)

This is also an unbiased estimator and the standard deviation is the same as for the DFT amplitude estimator. However, this technique requires the knowledge of the phase of the measured signal, so the previous technique was used.

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VII. Experiments The microsystem was tested using a solution of 2.3 × 109 particles/ml with 1.5 µm diameter magnetic nanoparticles. An 5 µA DC current was driven by the DAC through a 10 kΩ MTJ. The voltage signal was measured by an AD converter at a sample rate of 6 Hz after passing through a suitable anti-aliasing filter. The measurement time was about 8 minutes. The measured signal is presented in fig. 6, after the removal of a 47 mV DC signal. The solution was dropped on the sensor after about 1000 samples, and after about 1750 samples, the sensor was washed with distilled water. The figure clearly shows a 150 µV signal due to the presence of nanoparticles, demonstrating that the microsystem can be used for particle detection. 200 150

Idc = 5µA H dc= 15 Oe

wash wit h DI wat er

∆V (µV)

100 10 µL of part icles (1.5 µM)

50

∆V = 191µV

0 -50 0

500 1000 1500 2000 2500 3000 Number of samples (6 spl/ s)

Fig. 6. Time variation of the measured signal for evaluation of particle detection capabilities.

VIII. Conclusion Techniques to measure and extract biological information in a recently developed handheld biochip-based Microsystems were presented. Different types of measures were compared. Also studied were different filtering strategies based on noise signals presented in the system. These strategies allowed the filtering of the noise without excessively increasing the total time required to measure the signals at the sensors, while maintaining low computational complexity and low power consumption of the board. Finally, the viability of the July 7, 2006—4 : 31 pm

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system was demonstrated by testing it in a real bio signal detection experiment with good results. References [1] D. M. W. Shen, X. Liu and G. Xiao, “In situ detection of single micron-sized magnetic beads using magnetic tunnel junction sensors,” Appl. Phys. Lett., vol. 86, no. 25, p. 253901, June 2005. [2] M. S. P. et al., “Microsystem for biological analysis based on magnetoresistive sensing,” in Instrumentation and Measurement Technology Conference 2006, Sorrento, Italy, 2006. [3] M. Johnson, Magnetoelectronics. Academic Press, 2004, ch. 7, pp. 331–274. [4] H. A. F. D. L. Graham and P. P. Freitas, “Magnetoresistive-based biosensors and biochips,” Trends Biotechnol, vol. 22, no. 9, pp. 455–462, September 2004. [5] T. M. A. et al., “Characterisation and modelling of a magnetic biosensor,” in Instrumentation and Measurement Technology Conference 2006, Sorrento, Italy, 2006. [6] ——, “Magnetoresistive biosensor modelling for biomolecular recognition,” in XVIII International Measurement Confederation World Congress, Rio de Janeiro, Brazil, 2006. [7] A. V. Oppenheim and R. W. Schafer, Discrete.Time Signal Processing.

Prentice Hall,

1999. [8] S. Haykin, Adaptive Filter Theory. Prentice-Hall, Inc., 1996.

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