Proceedings of XXXVII Conference on desing of circuits and integrated systems (DCIS 2022), 16-18 November 2022, Pamplona, Spain.This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 956601

Noviello, Michele

Patón Álvarez, Susana

Quintero Alonso, Andrés

English

Universidad Carlos III de Madrid e-Archivo

Proceeding of: XXXVII Conference on desing of circuits and integrated systems (DCIS 2022), 16-18 November 2022, Pamplona, Spain. Time-encoded audio MEMS sensors optimized foredge computingMichele NovielloElectronic Technology Dept.Carlos III UniversityMadrid, Spainmnoviell@ing.uc3m.esAndres QuinteroRadio Frequency and SensorsInfineon Technologies AGVillach, AustriaAndres.QuinteroAlonso@infineon.comSusana PatonElectronic Technology Dept.Carlos III UniversityMadrid, Spainspaton@ing.uc3m.esAbstract—Conventional read-out electronics for MEMS capac-itive microphones require biasing circuitry, consisting of chargepump and high-ohmic link between the ASIC and the MEMS.Biasing circuitry influences the sensitivity and the attainableSNR. On the lookout for new solutions which simplify thecircuit scalability, it is of interest to envisage architecturesthat do not require the microphone biasing and the sensitivitydepends on other parameters. Time-encoding architectures allowto directly convert a capacitance signal into a frequency oneby means of capacitance-controlled oscillators. In order to boostthe sensitivity and the achievable SNR, we propose the cross-connected configuration rather than grounded sensors. Althoughthis technique does not lead to high quality microphones, thiswork is intended for Human-Machine Interface applicationsgiven their moderate SNR requirements and the need for lowcost, area and power.Index Terms—ADC, MEMS microphone, HMI, time-encodingI. BACKGROUND AND MOTIVATIONCapacitive MEMS sensors are widely used in Human-to-Machine Interface (HMI) applications (e.g. wearable electron-ics). A conventional read-out for the MEMS sensor is basedon a capacitance-to-voltage conversion by keeping constantthe charge stored on the plates of the MEMS capacitor [1].The constant-charge read-out is sufficiently sensitive if thesensor is biased by a large dc voltage (obtained by meansof a charge pump); also, to avoid charge leakage, the sensoris connected to a high-ohmic bias resistor. The need forlarge bias voltage and resistor hinders scaling; moreover,the high impedance node, directly connected to the sensor,is subject to interference [2]. All these reasons ignite theinterest in innovative techniques to read the sensor, whichshould be optimized to targeted applications and avoid theusage of the charge pump. The main constraints are powerconsumption, cost and available area, instead of high-qualityaudio acquisition and SNR performance.II. MEMS CAPACITANCE CONTROLLED OSCILLATORSTime-encoding techniques have proved to be suitable toimplement Sigma-Delta ADCs to digitize the signal detectedby the microphone by means of VCOs and digital electronics.This project has received funding from the European Union’s Horizon 2020research and innovation programme under the Marie Sklodowska-Curie grantagreement No 956601.(a) One grounded sensor(b) Two grounded sensors(c) Cross-connected sensorFig. 1: Different configurations of MCCO.The main advantages are the scalability and the digital natureof time-encoded signals. Mainly, VCO-based ADCs need avoltage-encoded input, which is obtained as described in partI. In absence of biasing circuitry, a direct capacitance-to-frequency conversion may be achieved by modulating theoscillation frequency of a ring oscillator (RO) with the MEMScapacitor. Thus, this paper deals with MEMS capacitancecontrolled oscillator (MCCO). The easiest way to connectthe sensor to the RO is the grounded configuration, which isshown in Fig. 1(a). This solution is simple but the sensitivityof the capacitance-to-frequency conversion is not enough tocompensate the small capacitive signal. As a consequence, forreasonable values of the oscillation frequency and the samplingfrequency (which are related to the power consumption), theresulting performance of the ADC in terms of SNR is low. Aninefficient way to increase the sensitivity is to use more sensors(cf. Fig. 1(b)). Our proposal is referred to as cross-connectedconfiguration, shown in Fig. 1(c)) to boost the performance ofthe read-out by using just one sensor. The sensor is connectedacross one of the CMOS inverters of the RO. In Fig. 2, thesensitivity of the Grounded Sensor (GS) configuration withone or two sensors is compared to the Cross-Connected Sensor(CCS) case. The latter provides more sensitivity than using twosensors.1Fig. 2: Modulus of sensitivity against MEMS capacitance.Fig. 3: Simulation results fitted by analytical formulae.III. ANALYTICAL STUDY AND DESIGN EXAMPLEThe improvement in sensitivity comes from a time-dependent Miller effect, which intuitively increases the numberof effective sensors connected to the RO. It can be shown thatthe oscillation frequency f osc and the sensitivity kMCCO of theRO can be expressed as:f osc =IV DD(C ′OXWLN + β CMEMS)(1)kMCCO =∂f osc∂ CMEMS=−β IV DD(C ′OXWLN + β CMEMS)2(2)where VDD is the supply voltage, C ′OX is the gate capacitanceper unit area, W and L are the gate width and length, N is thenumber of CMOS inverters and current I can be approximatedas I = µCox′2WL (V DD − V T)2 [3]. The parameter β allowsa general usage of these equations in all the case studies;basically, it represents the number of grounded sensors. Afterfitting it turns out that CCS can be approximately studied asan effective number β of grounded sensors which is more thanone (e.g. β is around three in the case depicted in Fig. 3). Also,Fig. 3 demonstrates that the analytical model is in accordancewith the results of transistor-level simulations in a 130nmtechnology node. The increase in sensitivity is not excessivelyhigh, but it is remarkable that the price paid is just a slightincrease in power consumption. At the same time, no biasvoltage and bias resistor have been required (and the relatedproblems are avoided). The time-encoded output of the MCCOis processed by digital electronics to achieve a complete first-order Sigma-Delta ADC as shown in Fig. 4 [4]. The maintrade-off is between power and SNR performance; evidently,an increase in power consumption improves the SNR of theADC, but at the same, as already stated, it is also one of themain constraints for wearable electronics applications. As adesign example, let us assume the sensor sensitivity as 9 fF/Paat 1 kHz, the rest value of the MEMS capacitor equal to0.8 pF, VDD equal to 1V and a five-stage RO in a 130nmCMOS technology node with the following transistor sizes:Ln = Lp = 2 µm, W n = 15 µm and W p = 30 µm. SamplingFig. 4: First-order noise shaped Sigma-Delta ADC.Fig. 5: Amplitude spectrum of a digitized sinewave.frequency is chosen as fs = 2.75MHz and analog bandwidthas ABW = 6.8 kHz. Figure 5 shows the amplitude spectrumof the digital output of the ADC in Fig. 4 given a sinusoidalinput of 94 dB SPL at 1 kHz. The SQNR estimate in the ABWis 58.9 dB. Instead, the SNR estimate due to phase noisecan be computed by referring it to the input, as explainedin [5]; the A-weighted SNR computation due to phase noiseis approximately 55 dBA. It is noteworthy that the currentconsumption of the simulated MCCO is around 83 µA.IV. CONCLUSIONThis manuscript proves that proposed CCS configurationprovides more sensitivity than GS one. Although the boost hasbeen achieved, the SNR computation is moderate (≥ 50 dB).Nevertheless, targeted HMI application do not require highquality microphones. It is noteworthy that CCS architecturedoes not require biasing circuitry for the MEMS sensor.The architecture is prone to be scaled. The average powerconsumption of the MCCO is 83 µW and the improvementof the SNR performance can be obtained at the price of anincrease in power consumption.ACKNOWLEDGMENTThe project, funded from the European Union’s Horizon2020 research and innovation programme, involves the collab-oration between Universidad Carlos III de Madrid and InfineonTechnologies AG Austria.REFERENCES[1] Sant, L., Bach, E., Gaggl, R., & Baschirotto, A. (2019, October). Acurrent-feedback amplifier with programmable gain for MEMS micro-phone read-out circuits. In 2019 Austrochip Workshop on Microelec-tronics (Austrochip) (pp. 1-5). IEEE.[2] Ersoy, S., Van Veldhoven, R. H., Sebastiano, F., Reimann, K., &Makinwa, K. A. (2013, February). A 0.25 mm 2 AC-biased MEMSmicrophone interface with 58dBA SNR. In 2013 IEEE InternationalSolid-State Circuits Conference Digest of Technical Papers (pp. 382-383). IEEE.[3] Abidi, A. A. (2006). Phase noise and jitter in CMOS ring oscillators.IEEE journal of solid-state circuits, 41(8), 1803-1816.[4] Kim, J., Jang, T. K., Yoon, Y. G., & Cho, S. (2009). Analysis and designof voltage-controlled oscillator based analog-to-digital converter. IEEETransactions on Circuits and Systems I: Regular Papers, 57(1), 18-30.[5] Cardes, F., Quintero, A., Gutierrez, E., Buffa, C., Wiesbauer, A., &Hernandez, L. (2018). SNDR limits of oscillator-based sensor readoutcircuits. Sensors, 18(2), 445.2

Time-encoded audio MEMS sensors optimized for edge computing

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Time-encoded audio MEMS sensors optimized for edge computing

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