V. Fabrication and Measurement Results
5.1 Fabrication results
5.1.1 Gas ROIC sensor module
Fig. 57 shows the fabricated gas ROIC sensor module. The gas ROIC sensor module consists of a gas sensor module at the top, a ROIC module at the middle, and an LTE module at the bottom. Each layer was connected using male and female FH01(2.54)-SS20P. The ROIC module includes KLDX- 0202-A so that it can be driven through the SMPS adapter connection. Each module is designed to fit
Fig. 133 Gas ROIC sensor module.
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the size of the sensor module and is designed to be as small as possible. The size is 12 cm wide, 7 cm long, and 7 cm high.
Fig. 58 shows the explosion-proof packaged gas ROIC sensor module. Explosion-proof packaging is designed to prevent explosion when explosive gas at high concentrations and high temperature gas sensors encounters. The upper part of the explosion-proof packaging has a hole to accept gas, and an antenna for LTE communication is connected to the left.
Fig. 134 Explosion-proof packaged gas ROIC sensor module.
Fig. 135 Gas sensor module and ROIC module.
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Fig. 59 shows the gas sensor module and ROIC module. The gas sensor module is packaged like a commercial product and the gas sensor is connected to the PCB through wiring bonding. The sensors on the right are designed to be disposable, and if the sensor fails, only the new sensor is replaced without discarding the entire sensor board. The gas ROIC module on the right in figure is composed of low dropout (LDO) for power supply, MCU and gas ROIC. The gas ROIC was connected to the PCB through a chip on board (COB).
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5.1.2 Gas ROIC sensor module
Fig. 60 shows the gas ROIC chip micrograph. The designed gas ROIC chip is consists of ROIC with wide dynamic range, SAR ADC to support low power, IADC for high resolution sensing, heater controller that provides various heater temperatures, temperature sensor to measure chip temperature, and SPI for chip control and gas data reading. A prototype of the proposed gas ROIC was fabricated in a 180-nm BCD CMOS process, and its chip area is 3.42 mm2.
Fig. 136 Gas ROIC chip micrograph.
Fig. 137 Chip micrograph of dual mode zoom IADC.
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Fig. 61 presents the chip micrograph of dual mode zoom IADC. This dual mode zoom IADC integrates functional component such as the 8-bit SAR ADC, the IADC, and the, excluding a decimation filter. The chip was manufactured using a 180-nm conventional CMOS process, and its average power consumption is 176 μW.
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5.1.3 Monitoring system
Fig. 62 shows the installed gas sensor module and monitoring system. The fabricated gas sensor module is installed to be less affected by environmental variables. Gas concentration can be checked in real time through the cloud monitoring system.(http://220.119.254.195)
Fig. 138 The installed gas sensor module and monitoring system.
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5.2 Measurement results
5.2.1 Self-calibration results
Fig. 139 Gas measurement setup.
Fig. 63 shows gas sensing measurement setup. A 5 V voltage was applied to the gas sensor module through the power supply, and the sensor operation was confirmed. The gas sensor data is obtained through interworking with a laptop computer using the module's Bluetooth. Timing issues that may occur due to multi-channel operation were removed, and a concentration equation was generated by analyzing the response of each sensor. When the gas was injected and the gas concentration was sufficiently saturated, the sensing operation was stopped, and the recovery step was performed for 2 hours.
Fig. 140 multi-channel gas sensor operation (CO 1ppm).
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Fig. 64 shows multi-channel gas sensor operation (CO 1ppm). The manufactured gas sensor module is designed to measure 10 types of gas sensors at the same time. It is confirmed that CO 1 ppm gas is injected and finally the corresponding concentration is expressed. In addition, the selectivity of the gas sensor is secured, and it seems that gas sensor only reacts to target gas. The measurement time is about 600 seconds, which depends on the gas type.
Fig. 65 shows edge computing-based pattern recognition (CO, NO2, H2). Channels 0, 1, and 6 represent the patterns of CO, NO2, and H2 sensors, respectively. When the pattern recognition algorithm determines that a specific gas has leaked, the value of the corresponding channel is changed to '1'. When CO gas is injected, it is confirmed that channel 0 becomes '1' through a pattern recognition algorithm. In the same way, it can be confirmed that NO2 and H2 are also well distinguished. In order to recognize patterns for other gases, training through ANN must be performed based on measured data for target gases.
Fig. 141 Edge Computing-based pattern recognition (CO, NO2, H2).
Fig. 142 Gas response change over time corresponding to gas concentration.
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Fig. 66 illustrates gas response change over time corresponding to gas concentration. To verify the methodology of gas response self-calibration, commercial gas sensor response data according to CO gas concentration was obtained through an experiment. The experiment was conducted by setting the gas concentration to 10, 20, 35, and 50 ppm, and CO gas was injected for 400 seconds to secure a sufficient response time.
Fig. 67 depicts CO gas response according to gas concentration and its logarithmic fitting response.
The response with the gas concentration is similar to the exponential graph, and the first logarithmic fitting was performed for easy analysis, which is Res_slope.
Fig. 74 shows Ro1 and Ro2 measurements to obtain the Ro slope. Ro1 was obtained by applying a voltage of 5 V to the heater of the CO sensor and measuring the resistance of the sensor when it reached a sufficiently saturated state. After obtaining Ro1, a 4V heater voltage corresponding to a heater
(a) (b)
Fig. 143. (a) CO gas response according to gas concentration and (b) its logarithmic fitting.
Fig. 144 Ro1 and Ro2 measurements to obtain the Ro slope.
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temperature of 380 degrees was applied to the sensor. Sufficient time had elapsed, Ro2 was obtained.
Ro_slope is obtained through two values, Ro1 and Ro2, and each heater temperature. The obtained Ro_slope and Res_slope are shown in Table Ⅶ below.
Ro_slope Res_slope
Sample 1 -0.0114 -0.2052
Sample 2 -0.0141 -0.1918
Sample 3 -0.0137 -0.2128
Sample 4 -0.0221 -0.1984
Sample 5 -0.0173 -0.2136
t1 t2 t3
Ro_slope1 Res_slope1 Ro_slope2 Res_slope2 Ro_slope3 Res_slope3 Sample 1 -0.0045 -0.2261 -0.0084 -0.2151 -0.0114 -0.2052 Sample 2 -0.0055 -0.2229 -0.0091 -0.2045 -0.0141 -0.1918 Sample 3 -0.0032 -0.2368 -0.0081 -0.2256 -0.0137 -0.2128 Sample 4 -0.0090 -0.2398 -0.0160 -0.2139 -0.0221 -0.1984 Sample 5 -0.0084 -0.2321 -0.0135 -0.2222 -0.0173 -0.2136
Correlation 0.3522 0.0039 0.1550
Table VIII shows the obtained Ro_slope and Res_slope for different times. The time point corresponding to the initial state is defined as t1, and after a week has elapsed, the aging process is performed by overheating and this is defined as t2. The time point after 5 months was defined as t3, and Ro_slope and Res_slope were measured, respectively. As the aging progressed, it was confirmed that Ro_slope increased and Res_slope decreased. To roughly confirm the methodology of gas response self-calibration, the correlation between Ro_slope and Res_slope was calculated at each time point, and
Table Ⅶ
Obtained Ro_slope and Res_slope.
Table ⅤIII
Obtained Ro_slope and Res_slope for different time.
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the result is shown in the Fig. 69. All the results had a low correlation. The results of this experiment were different from previous analyzes, so the theoretical analysis was again conducted.
In the process of expressing the gas sensor Ro slope as an equation, S1 represents an initial value at which aging does not proceed. However, since the sensor has already started aging from the point of manufacture, S1 cannot be obtained. In addition, the relationship between S1 and sensor response is not constant due to sample-to-sample variation. For this reason, the correlation between the gas sensor Ro slope and Res slope is very low. Instead of the existing S1, the aging S1' was introduced. If S1' is set to t1 corresponding to the initial time point, it can be expressed by the following equation.
𝑆1 = 𝑆1 +
( ) − (19) Also, the Ro slope S2 at t2, which is the time of aging due to overheating, is
𝑆2 = 𝑆1 +
( ) ( − ) (20)
Where t2 = t1+Δt. S2 is expressed as S1' and Δt as follows.
𝑆2 = 𝑆1′ + Δ
( ) ( − ) (21)
The Δt is expressed as follows by the equation (1).
𝛥𝑡 = τ exp (𝑙𝑛(𝑅𝑒𝑠𝑡2) − 𝑙𝑛(𝑅𝑒𝑠𝑡1)) (22)
Substituting the obtained Δt into the equation (21)
Fig. 145 Ro_slope and Res_slope fitting model and correlation for t1, t2, and t3.
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𝑆2 − 𝑆1 = ( ) ( )( − ) (23)
Equation(23) indicates that the difference of the Ro slope between t2 and t1 is correlated with the difference of each logarithmic gas response. To confirm this, correlation was analyzed based on the data at t1 and t2, and t2 and t3 were used as data to check whether calibration is possible.
t2 – t1 t3 – t2
Ro_slope21 Res_slope21 Ro_slope32 Res_slope32 Calibrated Res_slope32
Sample 1 -0.0039 0.0110 -0.0030 0.0099 0.0085
Sample 2 -0.0044 0.0184 -0.0042 0.0127 0.0176
Sample 3 -0.0049 0.0112 -0.0056 0.0128 0.0128
Sample 4 -0.0077 0.0259 -0.0054 0.0155 0.182
Sample 5 -0.0045 0.0099 -0.0044 0.0086 0.0097
Correlation 0.8408 -0.6470 -0.5459
Table IX shows the difference of Ro_slope and Res_slope for t1, t2, and t3. Unlike the correlation between Ro_slope and Res_slope at each time point, that of difference between the two viewpoints have a strong correlation. This means that Res_slope is inferred through Ro_slope. The calibrated Res_slope32 corresponding to the difference between the response slopes at time points t3 and t2 may have the following proportional relationship.
Ro_slope21 : Res_slope21 = Ro_slope32 : Calibrated Res_slope32 (24)
Therefore, the calibrated Res_slope32 is as follows.
Calibrated Res_slope32 = Res_slope21* Ro_slope32/ Ro_slope21
This result value is similar to Res_slope32, which means that aging response is inferred by Ro_slope.
Fig. 70 shows the Ro-slope and Res_slope fitting model for difference between t1, t2, and t3. It is confirmed that each fitting model and correlation are similar to each other.
Table IX
The difference of Ro_slope and Res_slope for t1, t2, and t3.
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Through Calibrated Res_slope32, Calibrated Res_slope3 corresponding to the estimated response at t3 is obtained as follows.
Calibrated Res_slope3 = Calibrated Res_slope32 + Res_slope2
Calibrated Res_slope32
Res_
slope2
Calibrated Res_slope3
Res_
Slope3
Sample1 0.0085 -0.2151 -0.2066 -0.2052
Sample2 0.0176 -0.2045 -0.1869 -0.1918
Sample3 0.0128 -0.2256 -0.2128 -0.2128
Sample4 0.0182 -0.2139 -0.1957 -0.1984
Sample5 0.0097 -0.2222 -0.2125 -0.2136
Table X presents a comparison of calibrated Res_slope3 and Res_slope3. and it is confirmed that the two variables are very similar. The aging process starts from t2, and the degree of aging is judged through the difference of Ro slope. If the response slope at t2 is defined as the initial response and the response slope at t3 is defined as the aged response, the calibrated Res_slope at t2 should be similar to the response of t2. To confirm this, Calibrated Res_slope2 was obtained by multiplying Res_slope2/Calibrated Res_slope3 corresponding to the aging coefficient and Res_slope3 corresponding to the aging state. The result of calibrated Res_slope2 for 5 samples is shown in table XI.
Fig. 146 Ro-slope and Res_slope fitting model for difference between t1, t2, and t3.
Table X
Comparison of Calibrated Res_slope3 and Res_slope3.
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Fig. 71 depicts comparison of initial, aged, and calibrated responses. When 35 ppm of CO gas is injected into the gas sensor, the initial gas response a can be obtained. The obtained initial gas response is substituted into the aged concentration equation, and the calibrated concentration equation. The aged gas concentration equation and the calibrated gas concentration equation show 44.5 ppm and 31.9 ppm, respectively. Since the gas concentration obtained from self-calibration method is closer to the initial gas concentration, it is considered calibrated.
Fig. 72 shows results of the initial, aged, and calibrated responses for each sample. The concentration information is obtained by substituting the response corresponding to each gas concentration equation, and the calibrated gas concentration is closer to the aged gas concentration.
Res_slope2 Calibrated Res_slope2
Sample 1 -0.2151 -0.2136
Sample 2 -0.2045 -0.2098
Sample 3 -0.2256 -0.2256
Sample 4 -0.2139 -0.2168
Sample 5 -0.2222 -0.2233
Table XI
The difference of Ro_slope and Res_slope for t1, t2, and t3.
Fig. 147 Comparison of initial, aged, and calibrated responses for sample 2.
68 Aged
concentration
Calibrated concentration
Aged error rate
Calibrated error rate
Sample 1 41.8 ppm 35.9 ppm 19.4% 2.6%
Sample 2 44.5 ppm 31.9 ppm 27.1% 8.6%
Sample 3 43.6 ppm 34.9 ppm 24.6% 0.3%
Sample 4 46.6 ppm 33.3 ppm 33.1% 4.9%
Sample 5 40.5 ppm 34.3 ppm 15.7% 2.0%
Table XII shows comparison of concentration and aged and calibrated error rate. If the gas concentration is obtained based on the calibrated Res_slope, the error rate can be lower than that without calibration. However, even if the response is calibrated, the error rate may be increased.
Table XII
Comparison of concentration and aged and calibrated error rate.
Fig. 148 Results of the initial, aged, and calibrated responses for each sample.
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t1 t2 t3
Ro_slope1 Res_slope1 Ro_slope2 Res_slope2 Ro_slope3 Res_slope3 Sample 1 5.88e-2 -103.6548 5.13e-2 -199.9733 4.08e-2 -340.2332 Sample 2 -6.01e-3 -6.1094 -4.22e-3 -6.9785 -3.96e-4 -9.2280 Sample 3 -2.97e-3 -21.6001 -4.82e-3 -11.9887 -4.72e-3 -12.1932
Correlation 0.7217 0.7296 0.9991
Table XIII shows fabricated sensor Ro_slope and Res_slope for different time. The fabricated gas sensor has very high response unlike commercial sensors. In general, the difference between the Ro and Ro slope values varies greatly from sample to sample. For the aging of the gas sensor, the method of injecting gas for a long time was used. However, this method did not proceed sensor aging well, and only the correlation between response and Ro slope was analyzed. The correlation between the obtained Ro slope and Res slope is larger than that of a commercial sensor because the number of samples is small and process consistency is guaranteed.
Table XIII
Fabricated sensor Ro_slope and Res_slope for different time.
Fig. 149 Fabricated sensor Ro_slope and Res_slope fitting model and correlation for t1, t2, and t3.
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Fig. 73 shows fabricated sensor Ro_slope and Res_slope fitting model and correlation for t1, t2, and t3. The obtained correlation shows high linearity, but the reliability of the analysis is low due to the small number of samples. The difference between t1 and t2 and the difference between t2 and t3 should be analyzed to confirm the correlation.
t1 - t2 t2 – t3
Ro_slope12 Res_slope12 Ro_slope23 Res_slope23 Obtained Res_slope23
Sample 1 7.56e-3 96.3185 1.05e-2 140.2587 133.2452
Sample 2 -1.79e-3 0.8691 -3.83e-3 2.2495 1.8608
Sample 3 1.85e-3 -9.6114 -1.04e-4 0.2045 0.5387
Correlation 0.8837 0.9646 0.9656
Table XIV shows the difference of fabricated Ro_slope and Res_slope for t1, t2, and t3. A very high correlation was confirmed using the Ro slope12 and Res slope12 data obtained at T1 and t2. Based on the results, the relationship equation between Ro slope12 and Res slope12 was obtained for each sample.
The Res slope23 for self-calibration was obtained by substituting Ro slope23 corresponding to each relationship equation. Based on the data obtained through each sample, the fabricated Ro-slope and Res_slope fitting model for difference between t1, t2, and t3 was obtained as shown in Fig. 74. The measured Res slope23 and Obtained Res slope23 obtained through the calibration equation are very
Table XIV
The difference of fabricated Ro_slope and Res_slope for t1, t2, and t3.
Fig. 150 The fabricated Ro-slope and Res_slope fitting model for difference between t1, t2, and t3.
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similar. In addition, since the slope of the fitting model is similar, the calibration error rate can be kept low even after a long period of aging.
Res_slope3 Calibrated Res_slope
Non-calibrated
error Calibrated error
Sample 1 -340.2332 -333.2185 1458% 9.6%
Sample 2 -9.2280 -8.8393 274% 19.2%
Sample 3 -12.1932 -12.5274 7% 10.0%
Table ⅩV shows comparison of fabricated sensor Res_slope and concentration error. The measured Res slope3 and the calibrated Res slope are relatively similar. Based on this, non-calibrated error and calibrated error is obtained based on 50 ppm of CO. It is confirmed that the uncorrected result has a very large error rate, because the difference in reactivity at the time of comparison is very large.
Therefore, non-calibrated error is not significant meaning. The error rate of the corrected result is similar to the results of the commercial sensor, which indicates that appropriate correction is possible.
[49] [50] [51] [52] This work
Readout-
Method R-to-V R-to-V R-to-F R-to-V R and I-to-V
CMOS
Process (μm) 0.25 0.35 0.35 - 0.18
Input Range
(Ω) Variable 100 ~ 20M 10k ~ 2 G 1k ~ 10 G 90~64M
Power (W) 17.5 m 6 m 10 m 25 m 7.8 m
Sensor Aging Self- Calibration
X X X X O
Pattern Recognition
(Edge Computing)
X X X X O
Heater control O X X X O
Multi-Mode
(R or I) O X X X O
Table ⅩV
Comparison of fabricated sensor Res_slope and concentration error.
Table ⅩVI
Performance comparison of gas sensor system.
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Table ⅩVI shows performance comparison of gas sensor system. Unlike other works in terms of functionality, the proposed indirect self-calibration gas sensor system is capable of sensor aging calibration and edge computing-based pattern recognition.Therefore, the human resource and cost for continuous gas sensor calibration can be reduced. In addition, by providing edge computing-based pattern recognition, it is possible to reduce server overload and build a gas sensing platform efficiently.
Since the heater temperature is freely adjusted through the heater controller, various gas sensors can be driven optimally.
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5.2.2 Gas ROIC chip performance
Fig. 75 shows the dynamic range(DR) of proposed ROIC for resistance and current mode. Resistance mode is measured from 90 ohms to 60 mega-ohms and shows DR performance of up to 128.8 dB and minimum 73.0 dB. Current mode is measured from 9 nA to 13.6 mA and shows DR performance of up to 123.8dB and minimum of 46.5dB.
Fig. 76 shows The SNR of proposed ROIC for resistance and current mode. Resistance and current measurements were performed for each of the three modes. Resistance mode is measured from 90 ohms to 60 mega-ohms and shows SNR performance of up to 93 dB and minimum 73 dB. In resistance mode measurement, the SNR is small in the low range, and the SNR tends to increase as it goes to the high range. This is because the size of the resistor has a greater influence than the noise of the current source
Fig. 151 The dynamic range (DR)of proposed ROIC for resistance and current mode.
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IDAC. In high resistance, the current source small IDAC has relatively high noise, and thus the SNR is greatly reduced. Current mode is measured from 9nA to 13.6mA and shows SNR performance of up to 94dB and minimum of 42dB. The current mode measurement also showed a tendency that the SNR is small in the low range and the SNR increased as the current range increased. The current source used in the low range current measurement has a high noise and thus this mode has a relatively low SNR.
The SNR continues to increase up to the middle current range, and then the SNR decreases in the high current mode. This is because the high current mode is measured through small RDAC. high current mode.
This is because the high current mode is measured through small RDAC.
Fig. 77 shows the measured output power spectral density (PSD) of the dual mode IADC. A sinusoidal signal corresponding to -0.4 dBFS with 349 Hz was used for measurement, and SNR and SNDR were confirmed through hann windowing. Measured SNR, signal-to-distortion-ratio (SNDR)
Fig. 152 The signal to noise ratio (SNR) of proposed ROIC for resistance and current mode.
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and spurious free dynamic ratio (SFDR) for the bandwidth of 2-kHz are 103.5 dB, 101.4 dB and 105.6 dB, respectively. The power of the DC level is -72.4 dB, which corresponds to a 420 μV offset.
Fig. 78 represents the measured PSD of the third order incremental ADC. The input signal appears at the DC frequency, and noise is pushed into the high frequency band greater than the inband frequency 200 Hz, which means that noise-shaping is well done. The 3rd order delta sigma incremental ADC has a 107.2 dB SNR based on a DC input of 1.2V.
Fig. 154 Measured output spectrum density of 3rd order incremental ADC.
Fig. 153 Measured output spectrum density of dual mode IADC.
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Fig. 79 shows Heater voltage versus time by operating with same frequency and heater voltage versus time by operating with different frequency. Verification was conducted from 1.5 V voltage to 3 V voltage, the proposed heater controller freely controls the heater voltage and temperature using MCU.
The voltage of the heater rises constantly, which prevents damage to the heater due to sudden power supply. By adjusting the operating frequency of the heater controller, the desired heater temperature and voltage increase rate are obtained.
ADC [53] [54] [55] [56] This work
Architecture IADC2
+IADC1 IADC1
+Multi Slope IADC2 IADC2 SAR+IADC1
+EC CMOS
Process (μm) 65 160 180 160 180
Conversion
rate(S/s) 500 2000 20 1500 4000
Dynamic
range (dB) 99.8 99.7 - - 109.1
Power (W) 10.7 μ 34.6 μ 0.24 μ 20 μ 176
SNRMAX (dB) - 98.4 93.4 81.9 103.9
FoM (dB) 173.5 174.6 169.6 157.1 179.7
Table ⅩVII shows performance comparison of gas sensor ROIC and system. The proposed dual mode incremental ADC has a high figure of merit due to high sampling rate and SNR performance.
Fig. 155 (a) heater voltage versus time by operating with same frequency and heater voltage versus time by operating with different frequency.
Table ⅩVII
Performance comparison of incremental ADC.