A capacitive pressure sensor interface using oversampling Δ-Σ demodulation techniques

Yamada, Mitsuhiro; Watanabe, Kentaro

doi:10.1109/19.552148

Cited by 64 publications

(24 citation statements)

References 10 publications

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“…When Φ = l (logic 1), Ch charges to the reference voltage V Ref while the capacitor C S is discharged to ground. Whereas, when Φ = 1, the total charge C h V Ref stored in the C h is transferred to C s producing an output voltage given by [16]:…”

Section: Switched Capacitor Circuitmentioning

confidence: 99%

ANN modeling of a smart MEMS-based capacitive humidity sensor

Kouda

Dibi

Barra

et al. 2011

Int. J. Control Autom. Syst.

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This paper presents a design of a smart humidity sensor. First we begin by the modeling of a Capacitive MEMS-based humidity sensor. Using neuronal networks and Matlab environment to accurately express the non-linearity, the hysteresis effect and the cross sensitivity of the output humidity sensor used. We have done the training to create an analytical model CHS 'Capacitive Humidity Sensor'. Because our sensor is a capacitive type, the obtained model on PSPICE reflects the humidity variation by a capacity variation, which is a passive magnitude; it requires a conversion to an active magnitude, why we realize a conversion capacity/voltage using a switched capacitor circuit SCC. In a second step a linearization, by Matlab program, is applied to CHS response whose goal is to create a database for an element of correction 'CORRECTOR'. After that we use the bias matrix and the weights matrix obtained by training to establish the CHS model and the CORRECTOR model on PSPICE simulator, where the output of the first is identical to the output of the CHS and the last correct its nonlinear response, and eliminate its hysteresis effect and cross sensitivity. The three blocks; CHS model, CORRECTOR model and the capacity/voltage converter, represent the smart sensor.

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Section: Switched Capacitor Circuitmentioning

confidence: 99%

ANN modeling of a smart MEMS-based capacitive humidity sensor

Kouda

Dibi

Barra

et al. 2011

Int. J. Control Autom. Syst.

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“…The nonlinear functions f 1 (T) and f 2 (T) determine the effect of the ambient temperature on the sensor characteristics [3]. This model provides sufficient accuracy in determining the influence of temperature on the sensor's response characteristics.…”

Section: Cps and Scimentioning

confidence: 99%

“…However, in practical situations, it is not easy to achieve ideal sensor characteristics, especially when the sensor is operating in a harsh environment. In order to compensate for some of the nonidealities and to obtain accurate readout, several digital and analog interface circuits have been proposed in the past with some success [1,2,3,4,5,6,7]. These techniques include both iterative and noniterative algorithms, and involve complex analog and/or digital signal processing to model the sensor characteristics.…”

Section: Introductionmentioning

confidence: 99%

Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment

Patra

Ang

Chaudhari

et al. 2005

EURASIP J. Adv. Signal Process.

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We present an artificial neural-network-(NN-) based smart interface framework for sensors operating in harsh environments. The NN-based sensor can automatically compensate for the nonlinear response characteristics and its nonlinear dependency on the environmental parameters, with high accuracy. To show the potential of the proposed NN-based framework, we provide results of a smart capacitive pressure sensor (CPS) operating in a wide temperature range of 0 to 250• C. Through simulated experiments, we have shown that the NN-based CPS model is capable of providing pressure readout with a maximum full-scale (FS) error of only ±1.0% over this temperature range. A novel scheme for estimating the ambient temperature from the sensor characteristics itself is proposed. For this purpose, a second NN is utilized to estimate the ambient temperature accurately from the knowledge of the offset capacitance of the CPS. A microcontroller-unit-(MCU-) based implementation scheme is also provided.

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“…However, such a redundant architecture results in large power consumption and a large chip area. Direct readout circuits are reported in [12,13], which employ a delta-sigma ADC. However, the circuits require large power dissipation due to the operational amplifiers (opamp) in the ADC.…”

Section: Introductionmentioning

confidence: 99%

A 0.026mm² capacitance-to-digital converter for biotelemetry applications using a charge redistribution technique

Tanaka

Kuramochi

Kurashina

et al. 2007

2007 IEEE Asian Solid-State Circuits Conference

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This paper proposes a direct capacitance-to-digital converter (CDC) for biotelemetry applications. The proposed circuit is based on a charge redistribution technique using a capacitive sensor and a ranging capacitor array. The circuit does not require accurate reference voltages, so it is robust for fluctuation of supply voltage. Output-code range can be dynamically zoomed in arbitrary capacitance range of sensor output by using the ranging capacitor array. An 8-bit converter with an active area of 0.026mm 2 , consuming 0.9nJ per sample, is demonstrated. The proposed circuit maintains its performance even in the condition of 28% fluctuations in supply voltage. Measurement results of the readout circuit are also demonstrated, which shows that the proposed circuit can work well in the presence of large parasitic capacitances.

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A capacitive pressure sensor interface using oversampling Δ-Σ demodulation techniques

Cited by 64 publications

References 10 publications

ANN modeling of a smart MEMS-based capacitive humidity sensor

ANN modeling of a smart MEMS-based capacitive humidity sensor

Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment

A 0.026mm² capacitance-to-digital converter for biotelemetry applications using a charge redistribution technique

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A capacitive pressure sensor interface using oversampling Δ-Σ demodulation techniques

Cited by 64 publications

References 10 publications

ANN modeling of a smart MEMS-based capacitive humidity sensor

ANN modeling of a smart MEMS-based capacitive humidity sensor

Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment

A 0.026mm2 capacitance-to-digital converter for biotelemetry applications using a charge redistribution technique

Contact Info

Product

Resources

About

A 0.026mm² capacitance-to-digital converter for biotelemetry applications using a charge redistribution technique