In this paper, we present a CMOS hybrid magnetic field sensor for real-time motor speed monitoring for smart energy-efficient autonomous systems. The sensor system consists of a micro Hall effect sensor (μHES) and a micro search coil magnetometer (μSCM) using TSMC 0.18μm 1P6M CMOS process with a die size of 1.5×2mm ² . The hybrid sensor is realized completely by the CMOS process without post-processing. A wide range of rotation speed can be measured by the presented passive μSCM without signal amplification (> 600 rpm) and the combination of the μSCM and μHES on the same chip allows the low rotation speed range measurement (<600 rpm). Furthermore, the μSCM has zero offset and very low-temperature dependency. The magnetic field of a permanent magnet on the rotating shaft of the motor is measured by the μSCM and the μHES to determine the rotation speed. The sensitivity of the μHES (at 5V bias voltage) and the μSCM inside a uniform magnetic field are 113.6 μV/mT and 11.3 μV/mT at 1 Hz without amplification, respectively. The noise equivalent magnetic induction of the μSCM was calculated and verified experimentally by utilizing a zero-drift instrumentation amplifier. Furthermore, a simplified equivalent circuit model for the μSCM is presented and verified experimentally. According to the equivalent circuit model, the linear frequency response region of the μSCM is up to 20 kHz, which suits the controlling motors in smart autonomous systems and other low-frequency applications.

In this article, CMOS compatible microelectromechanical systems (MEMS) multienvironmental sensor chip (MESC) is reported. The chip is comprised of temperature, humidity, and air velocity sensors. The MESC has several advantages. First, it incorporates low-cost three-mask CMOS compatible MEMS fabrication process to realize temperature, humidity, and air velocity sensing in the environment. Second, the air velocity sensor on MESC utilizes dual pairs of detectors and is completely released from the substrate, which achieved low-power consumption. Finally, a minimum level of crosstalk effect is observed among the sensors fabricated on MESC. The characterization results indicated that the developed temperature sensor achieved a sensitivity of 20.09 mV/°C, the humidity sensor obtained an average sensitivity of 7.83 fF/%RH, and the air velocity sensor achieved a sensitivity of 312 mV/m/s in dual detectors mode. Furthermore, the crosstalk among sensors on the chip was studied, which suggested that the air velocity sensor can affect the chip (temperature and humidity sensors) temperature by 2 °C–7 °C. This crosstalk is relatively less than the reported multisensor chips (10 °C). The experimental results achieved by the developed MESC make it promising for human thermal comfort (HTC) measurement in smart buildings in the era of the Internet of Things (IoT).

In this paper, a new analytical method to achieve the maximum signal-to-noise ratio (SNR) of a micro search coil magnetometer (µSCM) is presented. A planar spiral inductor was utilized to miniaturize conventional bulky search coil magnetometers. First, dimensional analysis was applied to identify three dimensionless parameters for the µSCM’s key performance indices (sensitivity (S_e), noise, and SNR). The effect of the parameters on the µSCM’s performance was carefully investigated, and a novel 4D nomogram was developed. Furthermore, an SNR analysis considering noise sources of a low-noise amplifier was performed. By combining the results from the nomogram and the effect of the noise sources from the amplifier circuit, optimum values for the dimensionless parameters were calculated. According to the calculation results, the dominant noise source varied with an increase in the track width ratio to the outer diameter. Seven different samples were fabricated by a single-mask lithography process. The sensitivity of 1612 mV/mT was demonstrated at a 50 Hz input magnetic field, which was better than the previous µSCM (S_e = 6.5 mV/mT) by more than 2 orders of magnitude. Finally, one of the fabricated µSCMs was employed to measure the online power consumption of a personal computer while different types of software were running.

Human thermal comfort is highly influenced by environmental parameters such as temperature, relative humidity (RH), and air velocity in addition to personal factors. The available sensors to measure these environmental parameters are stand-alone and can be very bulky and expensive, especially the air velocity sensor. Therefore, it is a challenge to measure human thermal comfort in smart buildings and most of the buildings utilize only temperature sensor to control the indoor climate. This work integrates multienvironmental sensors (temperature, humidity, and air velocity) on a single chip using low-cost CMOS-compatible MEMS technology. The multisensor chip (MSC) is successfully packaged and tested in experimental setups to characterize the temperature, humidity, and air velocity sensors integrated into it. The experimental results indicated that the air velocity sensor consumed low power (0.75 mW) while still achieving a much higher sensitivity of 123.8 mV/(m/s). Besides, the MSC’s RH sensor achieved an average sensitivity of 15.17 fF/%RH and covered the 7% to 82 % RH range, whereas the temperature sensor demonstrated a sensitivity of 25.2 mV/ $^{\circ}$ C during testing, covering a temperature range of 3 $^{\circ}$ C to 53 $^{\circ}$ C. Furthermore, the crosstalk study concluded that because of the low power operation of the air velocity sensor, a very negligible heat is transferred to the substrate. The overall chip temperature increases only by 0.1 $^{\circ}$ C due to the operation of the multisensors. This is quite low compared to the reported MSCs, where the chips’ temperature raised by 2 $^{\circ}$ C to 10 $^{\circ}$ C affecting the performance of the individual sensors located on the same chip. The results suggested that the developed MSC is promising for measuring environmental parameters in smart buildings in the Internet of Things (IoT) era.