High sensitive and rapid responsive n-type Si: Au sensor for monitoring breath rate

doi:10.4236/ns.2010.210130

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Vol.2, No.10, 1057-1060 (2010) Natural Science

http://dx.doi.org/10.4236/ns.2010.210130

High sensitive and rapid responsive n-type

Si: Au sensor for monitoring breath rate

Xuelan Hu1, Jiachang Liang2*, Xing Li3, Yue Chen4, Chao Zou5, Sheng Liu6, Xin Chen2

1Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin, China;

2College of Science, Civil Aviation University of China, Tianjin, China; *Corresponding Author: [email protected];

3Department of Automation, Tianjin Technical Normal Institute, Tianjin, China;

4College of Aeronautical Engineering, Civil Aviation University of China, Tianjin, China;

5China Electronic Standardization Institute, Beijing, China;

6Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin, China.

Received 25 March 2010; revised 30 April 2010; accepted 6 May 2010.

ABSTRACT

125 µm-breath sensor with high sensitivity and

rapid response was prepared by using n-type Si:

Au material. Its sensitivity coefficient and time

constant were 4 V.sec / L and 38 msec, respec-

tively. Its working principle was based on ano-

malous resistance effect, which not only increa-

sed the sensitivity, but also reduced its time

constant greatly. Its signal processing system

can select the breath signals and work stably.

Therefore, the small changes of breath system

can be measured and, especially, patient’s breath

rate can be monitored at a distance.

Keywords: Breath sensor; Signal processing

system; Deep impurity; Anomalous resistance effect

1. INTRODUCTION

For semiconductors containing shallow impurities, in-

cluding n-type silicon, the variation of its resistance Rs

with temperature obeys T-3 / 2 rule, i.e.

Rs = CT-3 / 2 (1)

where C is proportional constant. For single crystal

n-type silicon doped with deep impurities, near room

temperature, the relationship between its resistance Rd

and temperature T satisfies [1-3]

Rd = C exp [ – (EF – EA) / kT ] (2)

where k is Boltzmann constant, EF Fermi level and EA

the deep acceptor level in the band gap of silicon con-

taining deep acceptor impurities. For n-type Si: Au (n-

type silicon containing deep acceptor impurities of gold),

the anomalous resistance effect of exponential term in

Eq.2 can increase the sensitivity greatly. To compare the

effects of T-3 / 2 and exp [– (EF – EA) / kT], we take

kkTEETB

TBEET

dTdR

3 / ] / )( [ exp)(

)( )( 2

2/1



 (3)

Fermi level and the deep acceptor level of gold impu-

rity are equal to 0.57 eV and 0.54 eV, respectively, be-

low the conduction band in the band gap of our n-type Si:

Au material and, thus, EF – EA = 0.03 eV. At room tem-

perature (T = 300 K), we have

10 3.1

dR (4)

Our experimental measuring value is 3

105.1 . There-

fore, the sensitivity of 125 µm-breath sensor, made by

Wheatstone bridge using n-type Si: Au material as bridge

arms, can be increased by 103 times, comparing with con-

taining shallow impurities [4].

Our 125 µm-breath sensor can be used to monitor dif-

ferent breath flux and frequencies. In the Wheatstone

bridge of our 125 µm-breath sensor, the variation of the

offset voltage will play a vital role to the circuit. Gener-

ally speaking, the offset voltage is not a constant, but ra-

ther a function containing several unknown parameters.

It varies according with the changes of ambient tempe-

rature and brightness, service voltage and current, even

the technical defects during manufacturing. Another fac-

tor affecting the offset voltage is the asymmetry between

the bridge arms, especially the relevant resistors’ tem-

perature coefficient, which depends on the deep impurity.

If the offset voltage remains constant, the voltage output

of 125 µm-breath sensor only depends on its flux. In order

to eliminate the offset voltage drift, some compensation

methods and auto-adjusting circuits are adopted. Thus, the

sensor with such signal processing system can be used as

X. L. Hu et al. / Natural Science 2 (2010) 1057-1060

1058

the breath sensor to monitor the patient’s breath rate.

2. EXPERIMENTS

In n-type Si, the concentration of doped phosphorus

(shallow donor impurity) was equal to 15

100.1  cm-3

and in n-type Si: Au material the concentration of gold

(deep acceptor impurity) was equal to 15

101.1  cm-3.

The doped Au element was diffused in Si substrate by

deep doping method [5]. The main part of 125 µm-

breath sensor, made of n-type Si: Au material, was a

Wheatstone bridge using anisotropic etching n-type Si:

Au resistors. The length of each bridge arm was 125 µm.

In terms of electrostatic method, the bridge was bonded

to a borosilicate grass substrate, which had high thermal

isolation capacity. The structure and photograph of the

integrated fabrication of the 125 µm-breath sensor were

shown in Figures 1 and 2, respectively.

The schematic diagram of monitoring system of 125

µm-breath sensor was shown in Figure 3. A resistor R

was connected in series with the power supply circuit to

protect the sensor. The measurement was carried out in a

6 mm-radius tube with the measuring range from 0 to 27

L/min. Before measuring, the circuit should be supplied

Figure 1. The structure of the 125 µm-breath sensor.

Figure 2. Photograph of integrated fabrication of 125 µm-breath

sensor.

Figure 3. The schematic diagram of monitoring system of the

125 µm-breath sensor.

with power to preheat for 30 minutes. The static and tran-

sient characteristics of 125 µm-breath sensor were mea-

sured, including the output voltage as well as time con-

stant of 125 µm-breath sensor.

3. RESULTS AND DISCUSSION

3.1. Working Principle of 125 µm-breath

Sensor

Except for using n-type Si: Au material instead of hot

wires to form the electric bridge arms, this 125 µm-breath

sensor shares the same principle with the hot wire flow

meter. When air flows through the electric bridge, the

temperature variation between the two arms perpendicu-

lar to the air flow direction is much more than that be-

tween the two arms parallel with the air flow direction.

Thus, the temperature difference lead to the resistance

change of each bridge arm, and then lead to the change

of output voltage related with the flux at the output ends.

The sensitivity of the sensor depends on the resistance

variation of the electric bridge arms when air flows

through the sensor. The more the resistance varies, the

higher the sensor’s sensitivity is. When the sensor size

decreases to micro dimensions, the contact area between

the air and bridge arms is also largely reduced. So se-

lecting materials which have high sensitivity toward

temperature is the key problem. The sensitivity of 125

µm-breath sensor was increased greatly by using n-type

Si: Au material, because deep doping method can in-

crease the temperature sensitivity of the electric bri-

dge’s single arm resistance, as shown in Eq.3 and Eq.4.

3.2. The Static Characteristics of the

Monitoring System of 125 µm-breath

Sensor

Suppose the measurand is

, output isyand the re-

sponse time is zero, the function between the measurand

and the output should be

X. L. Hu et al. / Natural Science 2 (2010) 1057-1060

105

1059

, )( Cxfy



 (5)

where Cis a constant.

If the measurand varies dx , then the output’s varia-

tion should be

, )( ] / )( [dxxKdxdxxdfdy  (6)

where )(xK is the sensitivity coefficient. Took x and y as

flux and output voltage of 125 µm-breath sensor, respec-

tively, their relation was measured, as shown in Figure 4.

When the flux was more than 8 L/min, it had the linear

relation with the output voltage. In other words, their

slop could be used to represent the sensitivity coeffi-

cient of 125 µm-breath sensor. The sensitivity coeffi-

cient LVK sec/4  was obtained when we took the

stable supply voltage across bridge to be equal to 7 V

and the stable total supply current to be equal to 30 mA.

3.3. The Transient Characteristics of the

Monitoring System of 125 µm-breath

Sensor

Suppose the output voltage of breath sensor’s electric

bridge is ab

V, the relation between ab

V and time can be

measured by the digital oscillograph (for example, HP

54501). The horizontal scanning time base of such os-

cillograph could varies from 50 ns to 5 s. The relation

between ab

V and time can be represented

...,...3.2.1)], / ( exp1[ nmmtVV

mab  



(7)

where n

VVV ,..., 21 are the amplitudes of output voltage

variations caused by different factors. n



,..., 21 are the

time constants. Usually when m > 2 and m

V is too

small to negligible, the Eq.7 can be simplified to:

)] / ( exp 1[ )] / ( exp1[ 2211



tVtVVab  , (8)

where 1



is the time constant depending on electric

bridge arms and 2



the time constant depending on the

substrate. The measuring result was shown in Figure 5,

which indicated that the time constant (21



) of our

Figure 4. The relation between the sensor’s flux and the output

voltage of the 125 µm-breath sensor.

Figure 5(a). Definitions of the time constant and the response

time for 125 µm-breath sensor.

Figure 5(b). The response of output voltage versus time for

125 µm-breath sensor.

125 µm-breath sensor can be as small as 38 ms.

3.4. Signal Processing Circuit

The breath monitoring system can display the flux

from 1 to 27 L/min and the frequency from 1 to 200 /min.

Their normal values vary against different people. For

example, infants breathe about 44 times per minute and

adults breathe about 18 times per minute. So the system

must be adjustable, which could enable the doctors and

nurses to adjust the critical values to serve different peo-

ple. The critical adjusting parameters include the mini-

mal and maximal flux per minute, and the minimal and

maximal flow frequency per minute. When the system

detects breath state parameters exceeding the critical

values, the alarming circuit of the system starts to work

by lightening a red LED and sounding a buzzer to in-

form doctors and nurses. Due to its micro-size, this sen-

sor is very easily to install in the tubes which are used to

monitor the breath state.

The input signal from breath sensor is divided into

two paths after going through the buffering section of

the signal processing circuit. One is sent to the opera-

X. L. Hu et al. / Natural Science 2 (2010) 1057-1060

1060

tional amplifier 1; and the other is sent to operational am-

plifier 2. The signal through operational amplifier 2 is

also divided into two paths. One is fed to the A/D con-

verter to display breath flow speed; and the other is fed

to the Schmidt circuit to calculate the breath frequency.

The breath flux matrix and frequency matrix are used to

identify the breath state. If the breath flux or frequency

is either too high or too low, those circuits will send cor-

responding signals to the alarming circuit. Then the

alarming circuit will sound the piezoelectric ceramic

speaker and lighten the LED to notify doctors and nurses

to set those critical parameters.

Because other interfering signals, including doctors or

nurses’ walking, will destabilize the interface circuit, the

control circuit of interfering signals is necessarily in-

stalled to eliminate their effects. The operational ampli-

fier 1 is used to amplify those interfering signals. When

the input signal is small (＜ 10 mV), the potentiometer

in amplifier will let the Schmidt flip-flop to output a up

lever which can then let the A/D converter and the

counter to output null. In such case, even the small inter-

fering signal exists, the value shown on the flux and

breath frequency display will be null. When normal

breath signals input (＞ 10 mV), they will be sent to the

amplifier 2 after buffering. Then the signals will be sent

to the A/D converter to detect the flux and to the counter

to calculate the breath frequency.

The interfering signals, including doctors and nurses’

walking, will be sent to amplifier 1 and then fed to the

control circuit of interfering signals. After being digi-

tized, the characteristics of those signals will be stored in

storage systems. Some possible interfering waveforms

can also be prestored. When the system works, the input

signal will be compared with those already stored ones.

If they have the same characteristics, the control circuit

will send another signal to stop the breath flux and fre-

quency circuits working.

Sometimes the offset variation and drift might lead to

the whole system breakdown, especially when the

monitoring system has worked in a long time (usually

more then 10 hours). So the auto adjusting circuit should

be installed to maintain the offset voltage constant. The

operational amplifier 1 is used to amplify the interfering

signal and the offset voltage drift signal. The period of

breath signal is usually less than 6 s, whereas the time

duration, in which the offset voltage changes obviously,

is always more than 60 s. In such case, an offset voltage

timing circuit can be introduced to monitor the signal’s

duration. Because the duration of input signal is always

more than 60 s, the offset voltage timing circuit can send

another signal to the auto zeroing control switch and

output the compensating offset voltage from the auto

zeroing voltage sampling circuit to the operational am-

plifier 2 to eliminate the effects caused by the offset vol-

tage drift.

4. CONCLUSIONS

The 125 µm-breath sensor, made of n-type Si: Au ma-

terial, not only has a high sensitivity (LV sec/4) and

short time constant (38 ms), but also its fabrication tech-

nology is simpler than others [6,7]. So, it can be widely

applied.

The experiment indicates that its signal processing cir-

cuit can eliminate the voltage drift and other interfering

signals. The whole circuit can work stably to process all

breath signals. Therefore, patient’s breath rate can be

monitored at a distance. If continuing to reduce the size

of electric bridge arms and improve the thermal insula-

tion between the electric bridge and substrate, it is pos-

sible to make the time response even faster.

5. ACKNOWLEDGEMENTS

We would like to express our thanks to Dr H. J. Ma at the Institute of

Heavy Ion Physics of Peking University for his assistance and discus-

sion on the experiments and Ms jing Liang for her typesetting. This

work was supported by Basic Research Project of High Education

(ZXH2009C004) and Foundation of CAUC (09QD 06X).

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