Multi-Dimensional sensors and sensing systems

• US 8,884,382 B2
• Filed: 04/23/2013
• Issued: 11/11/2014
• Est. Priority Date: 07/17/2007
• Status: Active Grant

• Current Assignee: Sensirion AG (Sensirion Holding AG)
• Original Assignee: KWJ Engineering, Inc. (Interlink Electronics Incorporated)
• Inventors: Stetter, Joseph R., Shirke, Amol G.
• Primary Examiner(s): YUSHIN, NIKOLAY K
• Application Number:
  US13/868,583
• Publication Number:
  US 20130311108A1
• Time in Patent Office: 567 Days
• Field of Search: 257/414, 257/415, 204/406.408, 340/573.1, 340/573.12, 310/12.03, 600/365
• US Class Current: 257/414
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 2562/028 Microscale sensors, e.g. electromechanical sensors [MEMS]
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: G01N 27/00 Investigating or analysing materials by the use of electric, electrochemical, or magnetic means G01N3/00 – G01N25/00 take precedence; measurement or testing of electric or magnetic variables or of electric or magnetic properties of materials G01R
• CPC Class Codes: G01N 27/128 Microapparatus
• CPC Class Codes: G01N 33/005 for H2

Abstract

A universal microelectromechanical (MEMS) nano-sensor platform having a substrate and conductive layer deposited in a pattern on the surface to make several devices at the same time, a patterned insulation layer, wherein the insulation layer is configured to expose one or more portions of the conductive layer, and one or more functionalization layers deposited on the exposed portions of the conductive layer to make multiple sensing capability on a single MEMS fabricated device. The functionalization layers are adapted to provide one or more transducer sensor classes selected from the group consisting of: radiant, electrochemical, electronic, mechanical, magnetic, and thermal sensors for chemical and physical variables and producing more than one type of sensor for one or more significant parameters that need to be monitored.

Claims

1. A monitoring device, comprising:

a. at least one sensor or sensing system utilizing microelectromechanical systems (MEMS) technology that consumes less than 500 μ

W of power;

b. a processor;

c. non-transitory computer readable medium tangibly embodying a set of executable instructions; and

d. a power source;

wherein the set of executable instructions is configured to cause the processor to;

cause the power source to raise the power it consumes or operating temperature over a plurality of predetermined operating conditions over a prescribed time;

receive corresponding electrical changes in a sensing element of the sensor over each of the plurality of predetermined operating conditions over time; and

calculate target gas analyte composition for the received corresponding electrical changes.

2. The monitoring device of claim 1, wherein the electrical changes comprise resistance changes or voltage changes or combinations thereof.

3. The monitoring device of claim 1, wherein the calculation of target gas analyte composition is performed without any relative humidity (RH) dependence or temperature (T) dependence.

4. The monitoring device of claim 1, wherein the calculation of target gas analyte composition is performed using temperature (T) and relative humidity (RH) compensation using an embedded RH sensor in the sensor.

5. The monitoring device of claim 1, wherein the calculation of target gas analyte composition is performed using temperature (T) without the use of a relative humidity (RH) sensor.

6. The monitoring device of claim 2, wherein the act of causing the power source to raise its operating temperature comprises operating the power source in a pulsed mode.

7. The monitoring device of claim 5, wherein the target gas analyte is helium.

8. The monitoring device of claim 3, wherein the executable instructions are further configured to calibrate the sensor for temperature (T) correction and relative humidity (RH) correction;

and wherein the executable instructions are further configured to cause the processor to;

a. raise the operating temperature of the sensor from 10 degree C. to 30 degree C. in an environment free of RH by applying current to the sensor;

b. calculate R_max and R_min at a plurality of the operating temperatures, wherein R_max is the resistance of the sensor 0.4 ms after turning off current to the sensor and wherein R_min is the resistance of the sensor 20 ms after turning off current to the sensor;

c. calculating dR, wherein dR=R_max−

R_min;

d. calculating R_avg, wherein R_avg=(R_max+R_min)/2;

e. calculating λ

, wherein λ

=R_avg/dR;

f. calculate C1 and B1, wherein C1 is the slope and B1 is the intercept of the regression of R_avg/dR versus R_avg; and

wherein R_avg/dR=C1*R_avg+B1.

9. The monitoring device of claim 8, wherein the executable instructions are further configured to cause the processor to:

a. calculate unknown percentage of hydrogen (% H₂) at any of the operating temperature of the sensor, wherein

%H₂=([R_avg/dR]_unk−

[RaVg/dR]_0%H2/(100*C″

Ravg*Ravg+B″

]),

wherein C″

is the slope and B″

is the intercept of regression of C′

versus R_avg and wherein C′

is the slope of the regression of C1 versus % H₂.

10. The monitoring device of claim 9, wherein the executable instructions are further configured to cause the processor to:

a. obtain the resistance change of the sensor at a plurality of known concentrations of H₂;

b. obtain the resistance change of the sensor at a plurality of known concentrations of H₂O;

c. calculate a compensation factor (CF) for relative humidity;

d. calculate a compensated % H₂, wherein

%H₂=([R_avg/dR]_unk−

[R_avg/dR]_0%H2)/(100*[C″

R_avg*R_avg+B″

])−

CF.

11. The monitoring device of claim 4, wherein the calculation comprises best-fit of the received resistance data to planar surfaces in 3 dimensions.

12. The monitoring device of claim 4, wherein the calculation comprises best-fit of the received voltage data to planar surfaces in 3 dimensions.

13. The monitoring device of claim 11, wherein causing the power source to raise its operating temperature comprises operating the power source in a pulsed mode.

14. The monitoring device of claim 13, wherein the target gas analyte is helium.

15. The monitoring device of claim 14, wherein the executable instructions are further configured to calibrate the sensor for temperature (T) correction and relative humidity (RH) correction;

and wherein the executable instructions are further configured to cause the processor to;

a. raise the operating temperature of the sensor from 10 degree C. to 30 degree C. in an environment free of RH by applying current to the sensor;

a. calculate R_max and R_min at a plurality of the operating temperatures, wherein R_max is the resistance of the sensor 0.4 ms after turning off current to the sensor and wherein R_min is the resistance of the sensor 20 ms after turning off current to the sensor;

b. calculating dR, wherein dR=R_max−

R_min;

c. calculating R_avg, wherein R_avg=(R_max+R_min)/2;

d. calculating λ

, wherein λ

=R_avg/dR;

e. calculate C1 and B1, wherein C1 is the slope and B1 is the intercept of the regression of R_avg/dR versus R_avg; and

wherein R_avg/dR=C1*R_avg+B1.

16. The monitoring device of claim 15, wherein the executable instructions are further configured to cause the processor to:

a. calculate unknown percentage of hydrogen (% H₂) at any of the operating temperature of the sensor, wherein

%H₂=([R_avg/dR]_unk−

[Ravg/dR]_0%H2/(100*C″

Ravg*Ravg+B″

]),

wherein C″

is the slope and B″

is the intercept of regression of C′

versus R_avg and wherein C′

is the slope of the regression of C1 versus % H₂.

17. The monitoring device of claim 16, wherein the executable instructions are further configured to cause the processor to:

a. obtain the resistance change of the sensor at a plurality of known concentrations of H₂;

b. obtain the resistance change of the sensor at a plurality of known concentrations of H₂O;

c. calculate a compensation factor (CF) for relative humidity;

d. calculate a compensated % H₂, wherein

%H₂=([R_avg/dR]_unk−

[R_avg/dR]_0%H2)/(100*[C″

_Ravg*R_avg+B″

])−

CF.

18. The monitoring device of claim 1, wherein the at least one gas sensor senses a gas selected from the group consisting of hydrogen, helium, air, methane, carbon monoxide, hydrogen sulfide, chlorine, ozone, diesel particulates, gasoline fumes, ethanol, oxygen, carbon dioxide, and combinations thereof.

19. The monitoring device of claim 1, wherein the at least one sensor or sensing system utilizing MEMS technology comprises at least one surrounding environmental condition sensor.

20. The monitoring device of claim 18, wherein the at least one surrounding environmental condition sensor senses a surrounding environmental condition selected from the group consisting of temperature, pressure, radiation, moisture, and combinations thereof.