Study and design of an integrated system for the automatic calibration of dispersions within a transducers array and application to a PMUT array

Published : 15 July 2019

The purpose of this thesis is to study and design an integrated electronic system dedicated to the automatic and continuous compensation of dispersions within a MEMS (Microelectromechanical Systems) array.

With the dissemination and the continual expansion of Internet of Things (IoT) and Cyber-Physical Systems (CPS), man-machine and machine-machine interfaces require increasingly efficient and sophisticated sensors. In addition to advantages in cost, reliability, size and power consumption, MEMS based transducers enable sensors to integrate more and more intelligence in their front-end electronics. They also allow innovative topological configurations giving access to measurement ranges that are not addressable by their discrete counterparts.

Arrays of MEMS based transducers enable the spatial discretization of the transduction surfaces and improve the measurements yields and accuracies (gas detector, mass spectrometry, pressure distribution, etc.). They also enable the resolution improvement of electromagnetic and acoustic beams (location, navigation, communication, etc.).

Despite the considerable technological advancements that MEMS are continually enjoying, some application requirements are beyond the transducers intrinsic performances. It is then necessary to implement calibration systems to correct the transducers biases introduced during manufacture or evolving with the operating conditions.

The evaluation and compensation of these errors requires costly calibration process in a dedicated test laboratory, that are not compatible with massive production.

The aim of this thesis is to achieve an integrated electronic diagnostic alternative, an electromechanical BIST (Built-In Self-Test) specific to transducers arrays, combined with an automatic correction system, which will operate in coexistence with the main functions of the sensor interface.

The proposed use-case is that of PMUT (Piezoelectric Micromachined Ultrasonic Transducer) arrays. These devices offer alternatives and complementary solutions to electromagnetic sensors for detection and localization [1], gesture recognition [2] or wake-up signals detection [3]. For most applications, these resonant transducers operate in transmit / receive modes (TX / RX) and need to be actuate at their resonance frequency to optimize the transmission power. The emitted and received beam is focused and steered by phase control.

Errors and dispersion in the PMUT characteristics generates biases in their resonant frequency, gain and quality factor, leading to losses and distortions in the emitted and received beams. For example, a few percent of dispersions on the mechanical stiffness of the transducers can lead to several tens of percent loss on the acoustic power transmitted to a target.

As a first step, the doctoral student will get familiar with the quantities and physical phenomena characterizing PMUT arrays. Based on an analytical model developed within the host laboratory, he will be able to understand the sensitivities to dispersions and their impact on the beam power and directivity.

He will then define the electronic methods and architectures that will allow the system to converge towards the optimal operating conditions, for example by identifying the average resonance frequency of the array the required phase and gain correction coefficients to allocate to each transducer.

The architecture and implementation choices must allow the system to adapt itself according to dispersions and drifts in a continuous and autonomous way, without disrupting the main measurement functions.

The chosen solution will be implemented and validated in a mixed design environment in order to result in a functional demonstrator.

[1] Przybyla, R. J., Tang, H. -., Guedes, A., Shelton, S. E., Horsley, D. A., & Boser, B. E. (2015). 3D ultrasonic rangefinder on a chip. IEEE Journal of Solid-State Circuits, 50(1), 320-334.

[2] Ling, K., Dai, H., Liu, Y., & Liu, A. X. (2018). Ultragesture: Fine-grained gesture sensing and recognition. Paper presented at the 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2018, 1-9.

[3] Yadav, K., Kymissis, I., & Kinget, P. R. (2013). A 4.4-µ W wake-up receiver using ultrasound data. IEEE Journal of Solid-State Circuits, 48(3), 649-660.

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