An 8-Channel CMUT Chemical Array on a Single Chip

Quintin Stedman Kwan Kyu Park Butrus T. Khuri-Yakub Edward L. Ginzton Laboratory Hanyang University Edward L. Ginzton Laboratory Stanford University Seoul, South Korea Stanford University Stanford, CA, USA Stanford, CA, USA [email protected]

Abstract - Capacitive Micromachined Ultrasonic Transducers low cost and small size, all the should be on a single (CMUTs) can be used as small, highly sensitive chemical sensors chip. This can cause problems with locking between sensors. It in air. Single CMUT chemical sensors lack selectivity, but arrays also requires that the functionalization materials be localized to of multiple sensors can be used to allow selective chemical particular sensors, and not coated on the entire chip. detection. In this work, we demonstrate the simultaneous operation of eight CMUT chemical sensors on a single chip. We We have constructed an array of eight CMUT chemical then use seven sensors from this array to classify five different sensors on a single chip and demonstrated simultaneous chemical vapors and measure their concentrations. Finally, we operation of the eight sensors without locking. In prior work, use the same sensor array to classify three samples of coffee only two or three sensors have been used on a single chip [4,5]. beans grown on different farms. The sensor array is able to The sensors are prevented from locking by placing them at perform both classification tasks with 100% accuracy, different bias voltages so that they have different resonance demonstrating its selectivity. frequencies. We then demonstrate the use of the sensor array as an , classifying chemical vapors and measuring Keywords—CMUT, Chemical Sensor, Electronic Nose their concentrations, then distinguishing coffee beans of different origin. I. INTRODUCTION Capacitive micromachined ultrasonic transducers (CMUTs) II. METHODS can be used as gravimetric chemical sensors in air [1]. They CMUT chemical sensors were fabricated using a local have advantages such as small size, low cost, and high oxidation and wafer bonding process, as described in [3]. The sensitivity. A 50.5 parts per trillion limit of detection (3σ) has been demonstrated with a CMUT chemical sensor for dimethyl methylphosphonate, a simulant for sarin gas [2]. A chemical sensor that is very sensitive, but also small and inexpensive could be valuable in applications such as air quality monitoring, detection of dangerous chemicals, or breath analysis. A CMUT cell consists of a plate suspended over a vacuum gap. Many cells are connected in parallel to form a single chemical sensor device. Chemical sensitivity is imparted to a CMUT chemical sensor by coating the plate with a polymer or other material which absorbs chemicals from the air. The absorbed chemical increases the mass of the plate, shifting its resonance frequency according to

Δf /f = −Δm/2m (1)

So, chemicals can be detected by measuring changes in the resonance frequency of the CMUT. The chemicals absorbed in the polymer desorb when they are no longer present in the ambient air, so the sensor is reusable. Fig. 1 (a) Layout of a single CMUT chemical sensor. The individual cells are shown in blue and the isolation trenches are shown in black. (b). The functionalization materials are not perfectly selective. Photo of a sensor chip packaged on a pin grid array. (c) Layout of a To gain selectivity, arrays of chemical sensors with different chemical sensor chip with ten sensors, two resistance temperature coatings can be constructed [3]. To preserve the advantages of detectors, and a pressure sensor.

This work was supported by Fluenta AS, Norway.

TABLE I. CMUT CHEMICAL SENSOR CHARACTERISTICS

Number of Cells 721 Cell Radius 9 μm Plate Thickness 900 nm Gap Height 60 nm Resonance Frequency 31 MHz Collapse Voltage 65 V

TABLE II. POLYMERS FOR 8-SENSOR CHIP

Estimated Polymer Abbreviation Thickness (nm) Poly(vinyl alcohol) PVA 29 Poly(ethylene oxide) PEO 77 Polyepichlorohydrin PEO - Polycaprolactone PECH - Phenylmethyldiphenylsilicone 50 PCL (Ohio Valley #25) Poly(4-vinylphenol) OV-25 8 Polyvinylpyrrolidone PVP 5

Fig. 2 (a) The frequencies of the eight chemical sensors on a single chip, showing that the sensor are unlocked. (b) The Allan deviation of each of the eight chemical sensors, measured with all eight sensors operating.

Fig. 3 Photo of the chemical sensor with a poly(ethylene oxide) coating. frequency was observed, indicating that effects such as stress layout of the sensors and the chip are shown in Fig. 1. The or stiffness were more important than mass-loading. In these device properties are listed in Table I. Each chip contains ten cases, no thickness is noted. The drop coating process does not chemicals sensors, as well as two resistance temperature produce very uniform films, so in all cases, the thickness detectors (RTDs) and a pressure sensor. The chemical sensors should be regarded only as an estimate. are surrounded by isolation trenches to reduce acoustic When operating CMUT chemical sensors simultaneously, crosstalk and to prevent the functionalization materials from locking of the sensors to each other can be a problem. To avoid contaminating adjacent sensors. The trenches are drawn in this, the DC bias voltages of the devices were offset slightly black in Fig. 1 (a) and (c). The trenches are 80 μm wide and using resistive voltage dividers in order to separate the extend through the entire chip. They were fabricated using frequencies via the spring softening effect. deep reactive ion etching. The sensor frequencies were measured using op amp based A sensor chip was functionalized with the set of polymers oscillator circuits [1]. These circuits are designed to oscillate at listed in Table II. Seven different polymers were applied, the open circuit resonance of the CMUTs, and are read out while one sensor was kept with no polymer. Two sensors on using frequency counters. the chip were unused. The polymers were applied using drop casting. The polymers were dissolved in solvents and a drop of the solution was deposited on the sensor using a microsyringe. III. RESULTS The trenches surrounding each sensor kept the droplets of All eight sensors were able to operate simultaneously polymer solution from spreading onto adjacent devices. An without locking, as shown in Fig. 3 (a). To characterize the image of a sensor with a poly(ethylene oxide) functionalization noise of the sensors, the Allan deviation was measured. The coating is shown in Fig. 2. results are shown in Fig. 3 (b). All eight sensors were running as the Allan deviation was measured. The Allan deviation is The thickness of each polymer was estimated by measuring reasonable, but worse than has been achieved with single the reduction in resonance frequency induced by the mass of sensors. This is likely due to crosstalk between sensors. the polymer. For two polymers, an increase in resonance

Fig. 4 Response of the sensor array to (a) water, (b) , (c) , (d) isopropanol, and (e) . In each measurement, the chemical is introduced at 1 minute and removed at 4 minutes. (f) Principal component analysis plot of the normalized sensor responses.

extent to each chemical, but the pattern of responses is clearly TABLE III. LIST OF TEST CHEMICALS AND CONCENTRATION different for each chemical. DETERMINATION PERFORMANCE Fig. 4 (f) shows a principal component analysis plot of the Range of Concentration Error sensor responses at 4 minutes. The different chemicals are Chemical Concentrations (RMS % of Full Scale) (ppm) separated into distinct groups. For the principal component Water 156-1560 1.2 analysis, the sensor responses were normalized by the largest Methanol 836-8360 10.3 response of any sensor in each measurement. This puts Ethanol 389-3890 7.9 different concentrations on more equal footing and ensures that Isopropanol 287-2870 11.3 the differences between chemicals are due to a change in the Acetone 1510-15100 1.8 pattern of responses and not a change in magnitude. can be used to classify chemicals based The sensor array was tested on a set of five chemical on the sensor outputs. We implemented this using a support vapors. The chemicals and their range concentrations are listed vector machine (SVM) with a Gaussian kernel. We used the in Table III. The sensors were tested at ten different SVM implementation from the LIBSVM software library from concentrations across the range. The vapors were generated National Taiwan University [7]. For each chemical, using gas bubblers, and then diluted with nitrogen to the six measurements were randomly selected for the training data desired concentration. The PCL sensor was not used in these set while the remaining four were left for cross-validation. The experiments. data was normalized as described above for the principal component analysis. The SVM hyperparameters were chosen An example of a sensor responses to each chemical is using the training set. The model was then trained on the shown in Fig. 4 (a)-(e). In each measurement, the sensor was training set and tested on the cross-validation set. The model exposed to nitrogen for 1 minute, then to the chemical for 3 was able to classify the cross-validation set with 100% minutes, then to nitrogen again. Each sensor responds to some accuracy (24/24). to the different beans can be visually separated, though the separation is more subtle than for the chemical vapors. As with the chemical vapor experiment, the ability of a machine learning algorithm to classify the beans based on the sensor output was tested. A SVM with a Gaussian kernel was used, implemented by the LIBSVM software library [7]. For each type of bean, four samples were randomly chosen to train the algorithm, and two were chosen for cross-validation. The hyperparameters were chosen using the training data. The algorithm was able to classify the cross-validation data set with 100% accuracy (6/6).

IV. CONCLUSION We have demonstrated a CMUT-based electronic nose with eight sensors operating simultaneously on a single chip. This is an advance on prior work, where only two or three sensors on a single chip have been operated at once [2, 3]. Seven of these sensors were then demonstrated on two different chemical detection tasks. First, the sensors were used to classify five different chemical vapors and measure their concentration. Then, the same sensors were used to classify coffee beans grown on three different farms. The sensor array was able to perform both classification tasks with 100% accuracy. This illustrates the capability of the sensor array both to selectively detect single chemicals, and to distinguish complex scents composed of many chemicals.

ACKNOWLEDGMENT The CMUTs in this work were fabricated at the Stanford Nanofabrication Facility (SNF).

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