An in-tire-pressure monitoring SoC using FBAR resonator-based ZigBee transceiver and deep learning models

Abstract

In automobiles, tires play an important role in ensuring safe driving. Hence tire pressure monitoring system (TPMS) has become the most important safety system in automobile vehicles. In this work, a new indirect TMPS is proposed that measures the pressure of all the tyres indirectly using the available sensors already mounted for typical vehicle dynamics control purposes. The available sensors in vehicles usually contain a power supply module, a microcontroller, and transceiver and resonator circuitry. The available wheel speed sensor is modified by reducing the wireless receiver system in communication protocols to minimize the power consumption. The transceiver structure is modified using a suitable local oscillator (LO) frequency plan based on a temperature-compensated thin film bulk acoustic wave resonator (FBAR). In addition, the proposed TPMS SoC uses a deep-stacked autoencoder to detect the adherence conditions of the tires and a pressure loss based on wheel speed. This module will initially pre-process the speed signals to remove the speed error caused by manufacturing error. After pre-processing, different features such as statistical, frequency-domain and the fitted frequency-domain characteristics are extracted from the wheel speed signal and fed to the learning model for the judgment of the tire state. The proposed system was designed in Verilog synthesis and layout were made in cadence tool. For ASIC implementation, we use TSMC 65 nm CMOS Library for LNA/hybrid mixer design. The performance measures of the pressure detection model in terms of accuracy and errors are evaluated. Also, the performance metrics of the transceiver architecture in terms of gain, NF, IIP3, S11 and FOM are evaluated. The experimental evaluation describes that the model procures a maximum gain of 58 dB, NF of 2.15 dB and IIP3 of 6.6 dBm respectively. The die area is 0.45 mm2with 0.8 supply voltage and 0.83 mW power. In addition to this, the relationship between peak amplitude and speed vehicle regarding time and frequency domain features are evaluated.