NANOHOLE ARRAY BASED SENSORS WITH VARIOUS COATINGS AND TEMPERATURE CONTROL FOR COVID-19
20220074857 · 2022-03-10
Inventors
- Mona E. Zaghloul (Bethesda, MD, US)
- Jeanne A. Jordan (Arlington, VA, US)
- Stefanie Johns (New York, NY, US)
- Hayley Springer (New York, NY, US)
Cpc classification
A61B5/082
HUMAN NECESSITIES
B82Y20/00
PERFORMING OPERATIONS; TRANSPORTING
B82Y40/00
PERFORMING OPERATIONS; TRANSPORTING
G01N33/543
PHYSICS
B82Y15/00
PERFORMING OPERATIONS; TRANSPORTING
G01N21/554
PHYSICS
G01N33/54373
PHYSICS
International classification
Abstract
A nanohole array (NHA)-based plasmonic sensor (e.g., liquid/condensed phase sensor), their preparation, and their use to detect and analyze liquid samples, especially mixtures of chemicals and/or bio-chemicals and/or infectious diseases (e.g., viruses such as SARS-CoV-2 (COVID-19)).
Claims
1. A nanohole-array based plasmonic condensed phase sensor comprising: i) a substrate at least partially covered with a deposit; ii) a plasmonic layer on the deposit; and iii) one or more functional layers on the plasmonic layer; wherein the sensor comprises a plurality of nanoholes, and wherein the sensor further comprises one or more channels.
2. The sensor according to claim 1, wherein the one or more channels guide a condensed phase sample to be tested to the sensing area of the sensor.
3. The sensor according to claim wherein the sample is a condensed breath specimen collected from a subject being tested.
4. The sensor according to claim 1, wherein the sensor comprises one, two or three channels.
5. The sensor according to claim 1, wherein each channel is functionalized with one or more of the following reagents: i) Antibody that binds to SARS-CoV-2; ii) Antibody that binds to influenza virus.
6. The sensor according to claim 1 wherein the sensor comprises two channels, one coated with antibody that binds to SARS-CoV-2 and the other coated with an antibody that binds to influenza virus.
7. The sensor according to claim 1, wherein the substrate is an etchable substrate.
8. The sensor according to claim 1, wherein the substrate is silicon.
9. The sensor according to claim 1, wherein the substrate is covered with a deposit selected from Si.sub.3N.sub.4, SiO.sub.2, and a combination thereof.
10. The sensor according to claim 1, wherein the deposit is Si.sub.3N.sub.4.
11. The sensor according to claim 1, wherein the deposit has a thickness of between about 20 nm and about 600 nm.
12. The sensor according to claim 1, wherein the plasmonic layer comprises gold, silver, copper, aluminum, platinum, or any combination thereof.
13. The sensor according to claim 1, wherein the plasmonic layer comprises gold.
14. The sensor according to claim 1, wherein the plasmonic layer has a thickness of between about 5 nm and about 300 nm.
15. The sensor according to claim 1, wherein the functional layer comprises a metal organic framework, DNA, a protein, an aptarner, or any combination thereof.
16. The sensor according to claim 1, wherein the functional layer has a thickness of between about 5 nm and about 20 nm.
17. The sensor according to claim 1, wherein the functional layer has a thickness of about 15 nm.
18. The sensor according to claim 1. wherein the sensor comprises between 1 and about 20 layers of the functional layer.
19. The sensor according to claim 1, wherein the sensor comprises about 15 layers of the functional layer.
20. The sensor according to claim 1, wherein the functional layer comprises a biological layer that interacts with one or more target bio-molecules.
21. The sensor according to claim 20, wherein the one or more biomolecules comprise DNA, a protein, an aptamer, or any combination thereof.
22. The sensor according to claim 1, wherein the functional layer comprises copper 1,3,5 benzenebicarboxylate.
23. The sensor according to claim 1, wherein the sensor comprises circular nanoholes.
24. The sensor according to claim 1, wherein the nanoholes have a diameter ranging between about 10 and about 500 nm, between about 50 and about 350 nm, between about 100 and about 350 nm, between about 150 and about 350 nm, or between about 200 and about 350 nm.
25. The sensor according to claim 1, wherein the nanoholes have a diameter of about 25 nm, about 50 nm, about 75 nm, about 100 nm, about 125 nm, about 150 nm, about 175 nm, about 200 nm, about 225 nm, about 250 nm, about 275 nm, about 300 nm, about 325 nm, or about 350 nm.
26. The sensor according to claim 1, wherein the nanoholes have a diameter of about 50 nm or about 200 nm.
27. The sensor according to claim 1, wherein the period of the nanoholes is between about 50 nm and about 1000 nm, between about 300 nm and about 600 nm or between about 400 nm and about 500 nm.
28. The sensor according to claim 1, wherein the period of the nanoholes is about 400 nm or about 500 nm.
29. The sensor according to claim 1, wherein the plasmonic nanohole arrays are further coated with nanoparticles.
30. The sensor according to claim 1, wherein the sensor further comprises an integrated heater.
31. A method of making a condensed phase sensor comprising: depositing a covering on a substrate; (ii) patterning a nanohole array on the covered substrate; (iii) depositing an insulation layer on the covered substrate while leaving the nanohole array area uncovered. (iv) patterning a heater on the covered substrate; (v) patterning a membrane window on the backside of the coating on the coated substrate; (vi) etching the substrate to create the membrane. (vii) depositing a plasmonic layer on top of the sample, wherein the plasmonic layer is deposited at the central area with respect to the heater trace; and (viii) coating the plasmonic layer with one or more functional layers, and (ix) adding one or more channels.
32. A method of analyzing a condensed phase sample for the presence of a virus, the method comprising (i) providing a nanohole sensor according to claim 1; (ii) contacting the nanohole sensor with the condensed phase sample; and (iii) optically analyzing the condensed phase sample at one or more temperatures.
33. The method of claim 32, wherein the analysis is performed under step-wise changes in temperature.
34. The method of claim 32, wherein the analysis is performed by measuring the intensity change at the peak wavelength of the sample.
35. The method of claim 32, wherein the analysis is performed by measuring the intensity change at multiple wavelengths of the sample.
36. The method of claim 32, wherein analysis is performed. by measuring the value change in color channels of the sensor exposed to the sample.
37. The method of claim 32, wherein the analysis is performed using a spectrometer.
38. The method of claim 32, wherein the analysis performed using a camera.
39. An array comprising a plurality of sensors according to claim 1.
Description
BRIEF DESCRIPTION OF THE FIGURES
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DETAILED DESCRIPTION OF THE INVENTION
Definitions
[0139] As used herein, the term “nanohole-array based” refers to a nanostructured material which has been patterned and processed to have repeated indentations (such as circular indentations) across the surface of a material.
[0140] As used herein, the term metal organic framework (MOF) refers to a compound comprising one or more metal ions or clusters coordinated to one or more organic ligands to form a one-, two-, or three-dimensional structure.
[0141] As used herein, the term microfluidic channel refers to a channel with a hydraulic diameter below 1 mm. The terms microfluidic channel and microchannel are used interchangeably herein.
[0142] LSPR sensors are typically based upon ordered, nano-structured arrays. Nanohole arrays represent one approach to effect LSPR enhancement for sensor applications. LSPR involves oscillation at a certain wavelength for incident light. When the local environment changes, such as when gas molecules are adsorbed on the surface of the nanoholes, the oscillating wavelength shifts.
[0143]
Optimization of the Nanohole Sensor Based Arrays
Optimization of the Size, Shape and Period of the Nanoholes
[0144] For the measurements described herein, peak intensity changes resulting from adsorption of analytes are reported, since this monitoring approach exhibits less noise than measuring the shift in peak position itself. With the lower noise, the limit of detection may be lowered and the transient responses are more repeatable and more readily measured. However, to generate signals that are most sensitive and useful, it is helpful to use simulation of the field behavior to pre-determine which surface feature sizes, shapes and periods provide optimal spectral characteristics (see, e.g.,
[0145]
[0146] As can be seen from
[0147] The effect of the size of the nanohole was also studied.
[0148]
[0149] It is estimated that adding nanoparticles around the NHA patterns can further improve the enhancement of the electric field.
Fabrication Process
[0150]
[0151] (i) depositing 100 nm thick Si.sub.3N.sub.4 on a Si substrate using e.g., low-pressure chemical vapor deposition (LPCVD);
[0152] (ii) patterning the nanohole array using e.g., a deep UV stepper or E-beam lithography, and reactive ion etching (RIE);
[0153] (iii) optionally patterning a Pt heater surrounding the NHA pattern area using, e.g., a mask aligner and E-beam evaporator.
[0154] (iv) patterning the membrane window on the backside of Si.sub.3N.sub.3 layer using, e.g., a mask aligner and RIE etching, then etching the Si to create the membrane by etching, using, e.g., potassium hydroxide;
[0155] (v) depositing an adhesion layer of 5 nm titanium and a layer of 80 nm gold on top of the sample, using e.g., an E-beam evaporator;
[0156] (vi) coating the product of step (iv) with one or more layers of a metal organic framework (e.g., Cu-BTC).
[0157] Using this exemplary process, over 100 nanosensor chips may be made each time on a 100 mm wafer. The design of and process steps used to add the heater are compatible with portions of the device added before or after the heater. Furthermore, the design and operation of the heater are compatible with operation of the sensor as a plasmonic device.
[0158]
[0159] The bottom surface of the substrate 102 can be coated with a deposit, such as Si.sub.3N.sub.4. The Si.sub.3N.sub.4 deposited layer 106 is on the top surface of the substrate 102 and forms a thin planar layer 106 that spans a space 101 between the substrate legs 102. The plasmonic (e.g., gold) layer 108 is planar and on top of the Si.sub.3N.sub.4 deposited layer 106 and in one embodiment can cover the entire Si.sub.3N.sub.4 deposited layer 106. The functional (e.g., MOF) layer 110 is on top of the plasmonic (e.g., gold) layer 108 and in one embodiment can cover the entire plasmonic (e.g., gold) layer 108. The functional (e.g., MOF) layer has better adsorption of gases to be detected by the sensor 100, thereby increasing the performance of the sensor 100 (e.g., increasing the sensitivity, limit of detection). The plasmonic (e.g., gold) layer 108 does not significantly adsorb gases
[0160] Accordingly, the functional (e.g., MOF) layer 110, plasmonic (e.g., gold) layer 108, and Si.sub.3N.sub.4 deposited layer 106 each span a space formed by the substrate 102. One or more through-holes or openings 112 extend through each of those layers 106, 108, 110. The one or more through-holes or openings 112 may be formed on the deposit layer 106 by a fabrication process, and layers 108 and 110 are may be added thereafter. The openings 112 can be arranged in any suitable configuration, such as in rows and columns, as shown in
[0161]
Optimization of the Functional (e.g., Metal Organic Framework) Layer
[0162] Nanohole array sensors coated with different thicknesses of CU-BTC MOF were tested (5, 10, 15 or 20 layers) to determine the optimized thickness for gas sampling. For the analytes studied, the maximum sensor response was found for 15-layers of MOF coating.
[0163]
Measurement of Gas Sample Concentration
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[0165]
[0166] Despite the similarity in chemical structure and molecular mass for the two analytes (acetone and ethanol), it is notable that differences are observed in sensor response parameters, particularly for the sensitivity and limits of detection.
Optimization of Nanohole Based Array Temperature -Based Target Discrimination
[0167] When sensing an analyte with unknown concentration, it is difficult to determine the analyte's identity and concentration only with the response at room temperature because the information in the response is not sufficient to find two unknowns, i.e. the identity and concentration of an analyte. See e.g., Zhao et al., “Miniaturized nano-hole array based plasmonic sensor for the detection of acetone and ethanol at room temperature and insights into the kinetics of adsorption and plasmonic sensing,” DOI 10.1039/xxxxxxxxxx.
[0168] A useful approach to enable greater discrimination is to obtain sensing responses at different temperatures to inform on the identity of a molecule and its concentration. The interaction of acetone and ethanol with the MOF-coated sensor are reflected in the change of optical intensity at a fixed wavelength and how the temperature-dependent interactions affect the intensity changes.
[0169] Kinetic analysis can help one understand temperature-dependent response behavior.
[0170] For example, assuming that interaction of gas (G) with the MOF sensor structure (S) produces the adduct SG which leads to the change of optical intensity (Equation 1).
G+S.fwdarw.SG (1)
[0171] The forward rate constant of the above equation is defined as k.sub.a. Considering that the number of active sites on the sensor structure is conserved, one can write Equation 2:
S(θ)+SG.fwdarw.F.sub.θ(total available sites) (2)
[0172] It is assumed that Fe is a function of the sensor structure and temperature and that for a fixed temperature the number of total sites remains constant. The formation of SG determines the response kinetics of the sensor. As the amount of SG increases, the change in the intensity value increases. Therefore, the response of the sensor is directly proportional to the concentration of SG. The rate of sensor response can be described by the Equation 3:
d[SG]/dt=k.sub.a[S]C (3)
where C represents the concentration of gas.
[0173] Rewriting Equation (3) in terms of respective site occupancies provides Equation 4:
d[SG]/dt=ka[F.sub.θ−SG][G] (4)
where [G]˜C. Solving Equation 4 provides:
[SG](t)=F.sub.θ(1−exp k.sup.aCt) (5)
[0174] The maximum response corresponds to the situation when al active sites (F.sub.□) are occupied by the reaction product (SG).
[0175] Therefore, the response transient can be expressed by the Equation 6:
S(t)=S.sub.max(1−exp k.sup.aCt) (6)
[0176] Equation 6 can also be written as Equation 7:
S(t)=S.sub.max(1−e.sup.(−t/τ) (7)
where τ=1/k.sub.aC is referred to as the characteristic response time for sensing of gases.
[0177]
[0178] Table 1 summarizes the estimated time constants values for the detection of acetone and ethanol gases at each of the individual operating temperatures (95% confidence interval).
TABLE-US-00001 TABLE 1 τ.sub.296 τ.sub.303 τ.sub.308 τ.sub.313 τ.sub.318 Gas (s) (s) (s) (s) (s) Acetone 20 ± 3 18 ± 2 17 ± 2 14 ± 2 12 ± 2 Ethanol 14 ± 2 14 ± 2 12 ± 2 11 ± 1 11 ± 1
[0179] The characteristic time constants estimated from the model decrease with increasing operating temperature. The activation energies for the adsorption of acetone and ethanol are estimated from the temperature dependence of the characteristic time constants (t) as shown in Equation 8.
t=t.sub.0exp(E.sub.A/kT) (8)
where E.sub.A is the activation energy for the adsorption of gas on MOF structure, k is the Boltzmann constant, and T is the absolute temperature.
[0180]
[0181] The estimated activation energies for the interaction of 5 μmol/mol acetone and ethanol are 0.188±0.025 eV and 0.107±0.014 eV respectively. As estimated, the activation energy for interaction of gases over the MOF is higher for acetone than ethanol. For example, since the activation energy for the interaction of the studied analytes (i.e., acetone and ethanol) over the developed sensing material is different, one can understand why kinetic behavior can assist in the discrimination of the different gas types. Thus, it can be beneficial for the sensors described herein to be operated with a dynamically varied temperature, i.e., a temperature programmed method of operation((e.g., using an integrated microheater) and the transient stage of the sensor responses at each tested temperature can be measured.
[0182] In one embodiment, a temperature programmed method of operation including step-wise increases and/or decreases of temperature at varying rates, which may provide a signal stream with enriched analytical information. See, e.g., Rogers et al., “Development of optimization procedures for application-specific chemical sensing.” Sensors and Actuators B: Chemical, 163.1, 8-19, 2012.
EXPERIMENTAL
[0183] The present invention is now further illustrated by means of the following non-limiting disclosure.
Preparation of Nanohole Based Array Sensors
[0184]
[0185] The exemplary represented process for preparation of a gas sensor includes: (i) depositing 100 nm thick Si.sub.3N.sub.4 on a Si substrate with low-pressure chemical vapor deposition (LPCVD), (ii) patterning 200 nm circular hole arrays with a deep UV stepper/E-beam lithography and RIE etching, (iii) patterning the membrane window on the backside of Si.sub.3N.sub.4 layer with mask aligner and RIE etching, (iv) etching Si to create the membrane by KOH etching, and (v) depositing 5 nm Ti+80 nm Au on top of the sample with an E-beam evaporator. With this method, over 100 nanosensor chips can be made each time on a 100 mm wafer. Each chip contains 4 sensing areas (
[0186] An exemplary fabrication process for a sensor with a micro-heater includes: (i) depositing 100 nm thick Si.sub.3N.sub.4 on a Si substrate with low-pressure chemical vapor deposition (LPCVD), (ii) patterning 200 nm circular hole arrays with a deep UV stepper/E-beam lithography and RIE etching, (iii) depositing an insulating layer on the substrate while keeping the sensor area uncovered with a mask aligner and E-beam evaporator (iv) patterning the Pt micro-heater surrounding the sensor area with a mask aligner and E-beam evaporator, (v) patterning the membrane window on the backside of Si.sub.3N.sub.4 layer with mask aligner and RIE etching, (vi) etching Si to create the membrane by KOH etching, and (vii) depositing 5 nm Ti+80 nm Au on top of the sample with an E-beam evaporator.
[0187] An exemplary fabrication process for a liquid/condensed phase sensor with a micro-heater includes: (i) depositing 100 nm thick Si.sub.3N.sub.4. on a Si substrate with low-pressure chemical vapor deposition (LPCVD), (ii) patterning 200 nm circular hole arrays with a deep UV stepper/E-beam lithography and RIE etching, (iii) depositing an insulating layer on the substrate while keeping the sensor area uncovered with a mask aligner and E-beam evaporator (iv) patterning the Pt micro-heater surrounding the sensor area with a mask aligner and E-beam evaporator, and (v) depositing 5 nm Ti+80 nm Au on top of the sample with an E-beam evaporator.
[0188] The exemplary Cu-BTC MOF used in the studies described herein was coated layer-by-layer to generate the thin layer of MOF. Each 4-sensor chip was first submerged in a self-assembling-monolaver (SAM) solution (100 μmol/L 4-mercaptobenzoic acid/ethanolic solution) 37 for 1 hour. The method described in Zhao et al., J. Mat. Chem. A, 3, 1458-1464, 2015 was adapted to coat thin layers of MOF on the sample. 1,3,5-benzenetricarboxylic acid (BTC, 98% v/v, Acros Organics) and copper (II) acetate monohydrate (99% v/v, Sigma Aldrich) were dissolved separately in two vessels with ethanol to make 1 mmol/L solutions. During the coating process for each layer, the SAM-coated sensor chip was first dipped in BTC solution for 5 minutes and rinsed in ethanol for 1 minute. The chip was then transferred to the copper (II) acetate monohydrate solution for 5 minutes and then rinsed in ethanol for 1 minute. During each transfer between solutions, the chip was dried in air for 10 seconds. The coating process was repeated multiple times to afford the Cu-BTC MOFs with varied thicknesses. To avoid breaking the suspended platforms, a shaker (IKA KS 130 control with IKA AS 130.1 attachment) was used instead of a sonicator during the coating process. The shaking rate was set to 100/minute.
System Setup and Sensor Characterization
[0189]
Use of an Integrated Heater
[0190] In another embodiment, an integrated heater is added to supplement and/or substitute for the cartridge heater and maintains the planar structure of the sensor. For example, a 200-nm thick heater 120 may be placed around the NBA pattern to provide temperature control of the sensing platforms and avoid blocking the light transmit through the NHAs. An exemplary micrograph of a fabricated Pt ad croheater is shown in
[0191]
[0192] As shown in
[0193] The heater 120 extends around the holes 112 in the form of an unclosed square shape having two ends that are separated by a slight gap so that the heater 120 doesn't short circuit when a current is applied. The heater 120 can extend close to the edges of the gold layer 108 (
[0194] The heater 120 can be a metal lead line, wire, or thin plate. A voltage differential can be applied at the two ends via lead lines to generate a current that flows through the heater 120 to create heat that heats the gold layer 108, as well as the MOF layer 110 and the Si.sub.3N.sub.4 layer 106. The heater 120 is generally placed outside of the holes 112 to minimize any electrical disturbance that the metal may otherwise cause. The heater 120 is configured to create an even temperature distribution throughout the sensor layers 106, 108, 110 and achieve a desired temperature that maximizes the sensitivity of the MOF layer 110 with respect to the specific gas being detected. The leads can also be used to sense or detect the temperature of the heater 120 and the MOF layer 110. It should be noted that the heater 120 can have other suitable shapes and configurations. For example, the heater 120 can be a circular ring or one or more linear strips placed along the sides of the gold layer 108. The heater 120 can also extend between the holes of the nanohole array, though that could cause unwanted electrical disturbances.
[0195] The existence of the micro-hotplate may allow one to vary the local temperatures during the sensing periods. The sensing performance of NHA sensors may be measured at “m” different operating temperatures, where “m” is the number of temperatures applied during the sensing period.
[0196]
Measurement at Multiple Wavelengths
[0197] Measurement of the intensity change at multiple wavelengths instead of only at a single peak position may help to improve the selectivity of the sensor. An example of a multi-wavelength measurement is shown in
Additional Optical Measurements
[0198] In another embodiment, the spectrometer shown in the setup of
[0199]
[0200]
Detection of Viruses such as COVID-19
Determining the Limit of Detection and Analytical Sensitivity of the Assay
[0201] Serial 10-fold dilutions of each virus stock are made using an aqueous solution, then tested in triplicate on these functionali zed Au nanosensors to determine limit of detection for this device. Specificity of binding to these functionalized proteins is assessed using similar concentrations of UV-irradiated cultured stocks of common human coronaviruses (229E and OC43), Gamma irradiated human coronavirus 229E and OC43 are not available, so UV irradiated virus are used instead to perform the specificity studies.
Assessing Assay Specificity for Detecting SARS-CoV-2
[0202] Once the limit of detection for SARS-CoV-2 virus is determined, and assay specificity assessed, the internal control channel(s) are designed. Work in parallel functionalizing one channel on the device using fluorescently labeled reagents to assess coating of the Au surface (see reagent list above). Once established, the Au surface is coated with unlabeled reagents; e.g., anti-mucin SAC antibody in one channel and mouse anti-human IgA antibody in another channel. Again, serial 2-fold, 5-fold or 10-fold dilutions of each of these antibodies are made for functionalizing the Au nanosensor. Initially, purified human IgA and secretory mucin SAC proteins are sued to do these experiments. After those experiments, working with nasal and saliva matrices is introduced for detection of secretory mucin 5AC and human IgA. This allows determination whether one specimen matrix is more problematic than the other when using the u nansensor and microfluidic device. The differences in viral load levels found in nasal swabs versus oral secretions are balanced, with the potential inhibitory effects of the different matrices (saliva versus nasal mucus) on virus binding and lastly, the degree of cellularity in the two specimens (saliva versus nasal secretions) that may present problems around clogging of the nanopores on the device.
Rationale for Choosing S1 Domain, which contains the Receptor Binding Domain (RBD), over the S2 Domain of the Spike Protein
[0203] There is less amino acid homology for the S1 domain, which includes the RBD compared to S2 domain between SARS-CoV and SARS-CoV-2 (64% vs. 91%).The S1 subunit is found not only on intact virus prior to binding to ACE2 receptors (pre-fusion step), but is found as free S1 subunits including RBD that are shed from the SARS-CoV-2 virus after the virus binds to the ACE2 receptors on the cell when fusion of the virus and cell occurs.
[0204]
Detecting the Virus using the Nanohole Array Sensing System
[0205]
[0206] In one embodiment, the sample to be tested (such as a condensed breath sample collected from a subject being tested) interacts in a channel (e.g., a microfluidic channel) with one or more of the following reagents:
[0207] Antibody that binds to SAKS-CoV-2; and/or
[0208] Antibody that binds to influenza viruses;
prior to adsorption of the sample (e.g., on the functional layer of the sensor) and analysis/detection of the virus by the nanohole-array based plasmonic condensed phase sensor. Accordingly, the detector includes a channel and a sensor. The channel can be, for example, a tube or the like that receives a user's breadth and is in gas andlor fluid communication with the sensor. The sensor can include, for example, a nanohole. The sample can condense in the channel or in the nanohole in the sensor. Once in the nanohole, the sample then reacts with the antibody. Light on the nanohole is detected to determine if the sample contains a respiratory virus e.g., SARS-COV-2 and/or influenza), such as shown in
Design and Train an ANN-Based Algorithm to Classify the COVID-19 Virus
[0209] Open-source machine learning libraries, such as TensorFlow and Pytorch are used to build an artificial neural network (ANN)-based algorithm and a supercomputer is used to train the system. With the assistance of the supercomputer, each training process can be finished in 30 minutes. The ANN structure is modified and the hyperparatneters optimized to achieve a 90% accuracy of COVID-19 recognition.
EXAMPLE 1—MEMS MANUFACTURE
[0210] Microelectromechanical systems (NTEMS) manufacture may be split into three main stages. See, e.g.,
Stage 1—Patterning of NHA into Si.SUB.3.N.SUB.4
[0211] Dependent upon the NHA diameter size, a maskless aligner may be the most feasible (down to 200 nM). Factors affecting performance may include the photoresist, equipment and equipment settings. Pre Si.sub.3N.sub.4 coated wafers may also be used. See
Stage 2—Patterning of Backside and Etching of Membrane
[0212] A maskless aligner may be used to pattern the bottom side of the substrate, followed by RIE etching for the removal of Si.sub.3N.sub.4 and anisotropic wet etching for the removal of Si. See
Stage 3—Functionalization with Au MOF
[0213] Metallization may be performed in several ways:
[0214] Deposition of Cu-BTC MOF may be performed by [0215] Self-Assembled Monolyaer (SAM) solution (100 mmol L-1 4-mercaptobenzoic acid/ethanolic: solution for 1 hour. [0216] Alternating between 1 mmol/L in ethanol solutions of 1,3,5-benzenetricarboxylic acid (BTC, 98% v/v-Aeros Organics) and copper (II) acetate monohydrate (99% v/v, Sigma Aldrich) until a desired thickness is achieved.
Testing
[0217] Stage 1 testing was performed using dummy wafers and Microposit S1800 photoresist with the maskless aligner. Six samples (Samples 1-6) were prepared and tested at varying step sizes, attenuation level and power. Seven different tests patterns were trialed: 2000, 1000, 750, 400, 300 and 250 nm diameters. The best results were observed for Sample 6 (pure Si wafer, 525 ±25 μm thickness, 100 nm ϕ).
Settings
[0218] Exposed with 50 nm step size, 1 mJ/'cm.sup.2 dose and 75% attenuation on the UC-laser. The wafers with the six samples were then developed with MF319 for 60 seconds.
Results
[0219]
[0220] The table below provides additional results regarding diameter, pitch and spacing. Values were obtained using optical microscopy.
TABLE-US-00002 Target Measured Target Measured Target Measured Diameter Diameter % Pitch Pitch % Spacing Spacing % Test (nm) (nm) Error (nm) (nm) Error (nm) (nm) Error 1 2000 2320 −16 4000 3880 0.03 2000 1630 0.185 2 1000 1050 −5 2000 1960 0.02 1000 882 0.118 3 750 706 5.87 1500 1440 0.04 750 738 0.016 4 500 499 0.2 1000 996 0.004 500 425 0.15 5 400 493 −23.25 800 867 −0.08 400 275 0.313 6 300 — — 600 — — 300 — — 7 250 — — 500 — — 250 — —
[0221]
EXAMPLE 2—SPUTTERING AND SEM ANALYSIS
[0222] Sputtering testing was performed on Sample 6 NHA described in Example 1 (pure Si wafer, 525±25 μm thickness, 100 nm ϕ). The NHA substrate was sputtered with Au using a sputtering tool to an approximate thickness of 10 nm (matching the Au grain size), Two coating variances were tested: (i)10 nm thick coats, and (ii) 2×5 nm thick Au coats. The SEM results of the sputtering are shown in
[0223] The table below provides additional results regarding diameter, pitch and spacing following sputtering of samples (i) and (ii) using SEM inspection. [0224] (i) 10 nm Thick Coat
TABLE-US-00003 Target Measured Target Pitch Target Diameter Diameter Pitch Length Spacing Spacing Test (nm) (nm) (nm) (nm) (nm) (nm) 1 2000 1930 4000 3890 2000 2000 2 1000 669 2000 2010 1000 1341 3 750 706 1500 1440 750 734 4 500 499 1000 996 500 497 5 400 347 800 1030 400 683 [0225] (ii) 2×5 nm Thick Coats
TABLE-US-00004 Target Measured Target Pitch Target Diameter Diameter Pitch Length Spacing Spacing Test (nm) (nm) (nm) (nm) (nm) (nm) 1 2000 1020 2000 2000 1000 1000 2 1000 636 1500 631 750 869 3 750 527 1000 1000 500 464 4 500 325 800 789 400 325 5 400 170 600 602 300 396
EXAMPLE 3—DATA; IMAGE ANALYSIS
[0226] One approach to analyze the datalimages is to treat the MIA as two separate components: (1) hole analysis (particle size) and (2) cell analysis (Voronoi diagram), See
[0227] The table below shows the results of the hole analysis. Target area=785,298 nm.sup.2, diameter achieved=approx. 633 nm, average Feret diameter (measure of an object across a specific direction) is 699 nm.
TABLE-US-00005 Area Feret 1 331932 718.014 2 328045.2 728.44 3 329133.5 736.083 4 323847.5 702.251 5 323070.1 721.578 6 319027.9 704.682 7 280470.9 745.84 8 319338.8 705.343 9 327734.3 681.122 10 325402.2 683.515 11 326179.6 681.35 12 327889.8 695.801 13 325557.7 678.607 14 323692 688.504 15 269743.3 761.518 16 325091.3 683.515 17 311876.2 681.122 18 313897.3 662.727 19 314363.7 676.197 20 315763 682.149 21 256839.2 687.936 22 314052.8 733.438 23 312653.5 674.355 24 311098.8 665.42 Average (nm) 314862.5 699.146 STD (nm) 19044.18 26.72561 Coefficient of 6.05 3.81 Variation (CV) (%)
[0228] The table below shows the results of the cell analysis. Target width=2000 nm, target height=40000 nm, target area=8,000,000 nm.sup.2, As can be see, the CV for each of area, width and height is less than 1%.
TABLE-US-00006 Area Width Height 1 7510059 2007.481 3940.15 2 7606762 1970.075 3927.681 3 7572870 1970.075 3927.681 4 7576445 1995.012 3902.743 5 7601632 1970.075 3915.212 6 7587950 1970.075 3915.212 Average (nm) 7575953 1980.466 3921.447 STD (nm) 34940.88 16.5728 13.0776 Coefficient of 0.46 0.84 0.33 Variation (CV) (%)
EXAMPLE 4—TiW And Au DEPOSITION
[0229] A 5 nm thick layer of titanium tungsten (TiW) was deposited on a SiO.sub.2 substrate. This was followed by deposition of an 80 nm thick layer of Au. The process was implemented using a sputtering tool on the slowest setting (VG Microtech SC500 sputter tool, 29 Amps) to achieve a high-quality surface finish. The approximate process times were 35 minutes for TiW deposition and 85 minutes for Au deposition. Heat may be used to allow faster deposition rates. An SEM of the resulting product is shown in
Surface Roughness Study
[0230] Interferometry was used to characterize the surface roughness of the Au deposition on the SiO.sub.2 water. The results are shown in the table below. See also
TABLE-US-00007 Center Left Right Top Bottom Average 13.84 9.70 25.72 21.56 13.80 Roughness (Ra) (nm) Root Mean 16.75 12.15 29.82 25.36 16.90 Square Roughness (Rq) (nm) Peak Roughness 125.24 107.05 150.41 143.64 123.62 (Rt) (nm)
Photoresist Characterization
[0231] Photoresist testing was conducted on the SiO.sub.2 wafer spun at 4000 rpm for 30 seconds for two different photoresists with theoretical film thickness of 800 μm (Sample 1) and 410 μm (Sample 2). For a photoresist to achieve the best results, the thickness should be no greater than twice target parameter. For example, for a 200 nm diameter NHA, the photoresist thickness should be no greater than 400 nm.
[0232] Results are shown in the table below. See also
TABLE-US-00008 Thickness Thickness (nm) (nm) Position Sample 1 Sample 2 Center 613087 397.95 Top 616.09 379.59 Bottom 601.47 386.22 Right 637.11 393.08 Left 587.02 373.38
Aligner Mask Design
[0233] To prepare the mask for rear face processing, the from and rear faces should be aligned, A yield of 136 sensors may be achieved. See
EXAMPLE 5
Surface Roughness Studies
[0234] Interferometry was used to characterize the surface roughness of two different depositions on the SiO.sub.2 wafer The results are shown in the table below, as can be seen, the SiO.sub.2, +Cr+Au deposition has a lower average surface roughness and the SiO.sub.2+Tiw+Au deposition results in a greater average peak roughness
TABLE-US-00009 Center Top Bottom Right Left SiO.sub.2 + Average 20.79 14.58 13.18 20.44 12.35 Cr + Au Roughness (Ra) (nm) Root Mean 14.33 17.2 16.02 23.24 14.33 Square Roughness (Rq) (nm) Peak Roughness 81.37 93.6 94.01 111.74 81.37 (Rt) (nm) SiO.sub.2 + Average 13.84 21.56 13.80 25.72 9.70 TiW + Au Roughness (Ra) (nm) Root Mean 16.75 25.36 16.90 29.82 12.15 Square Roughness (Rq) (nm) Peak Roughness 125.24 143.64 123.62 150.41 107.05 (Rt) (nm)
Photoresist Optimization
[0235] Preferred results were achieved using a dose of 70 and a step size of 50.
[0236] Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as described above. It is intended that the appended claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby. All publications, patents and patent applications cited in this application are herein incorporated by reference to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated herein by reference.