Calibration of Ring Oscillator-Based Integrated Temperature Sensors for Power Management Systems.

This paper details the development and validation of a temperature sensing methodology using an un-trimmed oscillator-based integrated sensor implemented in the 0.18-µm SOI XFAB process, with a focus on thermal monitoring in system-on-chip (SoC) based DC-DC converters. Our study identifies a quadratic relationship between the oscillator output frequency and temperature, which forms the basis of our proposed calibration mechanism. This mechanism aims at mitigating process variation effects, enabling accurate temperature-to-frequency mapping. Our research proposes and characterizes several trimming-free calibration techniques, covering a spectrum from zero to thirty-one frequency-temperature measurement points. Notably, the Corrected One-Point calibration method, requiring only a single ambient temperature measurement, emerges as a practical solution that removes the need for a temperature chamber. This method, after adjustment, successfully reduces the maximum error to within ±2.95 °C. Additionally, the Two-Point calibration method demonstrates improved precision with a maximum positive error of +1.56 °C at -15 °C and a maximum negative error of -3.13 °C at +10 °C (R2 value of 0.9958). The Three-Point calibration method performed similarly, yielding an R2 value of 0.9956. The findings of this study indicate that competitive results in temperature sensor calibration can be achieved without circuit trimming, offering a viable alternative or a complementary approach to traditional trimming techniques.

Tiny CNN for Seizure Prediction in Wearable Biomedical Devices.

Epilepsy is a life-threatening disease affecting millions of people all over the world. Artificial intelligence epileptic predictors offer excellent potential to improve epilepsy therapy. Particularly, deep learning models such as convolutional neural networks (CNN) can be used to accurately detect ictogenesis through deep structured learning representations. In this work, a tiny one-dimensional stacked convolutional neural network (1DSCNN) is proposed based on short-time Fourier transform (STFT) to predict epileptic seizure. The results demonstrate that the proposed method obtains better performance compared to recent state-of-the-art methods, achieving an average sensitivity of 94.44%, average false prediction rate (FPR) of 0.011/h and average area under the curve (AUC) of 0.979 on the test set of the American Epilepsy Society Seizure Prediction Challenge dataset, while featuring a model size of only 21.32kB. Furthermore, after adapting the model to 4-bit quantization, its size is significantly decreased by 7.08x with only 0.51% AUC score precision loss, which shows excellent potential for hardware-friendly wearable implementation.

Towards Real-Time Monitoring of Thermal Peaks in Systems-on-Chip (SoC).

This paper presents a method to monitor the thermal peaks that are major concerns when designing Integrated Circuits (ICs) in various advanced technologies. The method aims at detecting the thermal peak in Systems on Chip (SoC) using arrays of oscillators distributed over the area of the chip. Measured frequencies are mapped to local temperatures that are used to produce a chip thermal mapping. Then, an indication of the local temperature of a single heat source is obtained in real-time using the Gradient Direction Sensor (GDS) technique. The proposed technique does not require external sensors, and it provides a real-time monitoring of thermal peaks. This work is performed with Field-Programmable Gate Array (FPGA), which acts as a System-on-Chip, and the detected heat source is validated with a thermal camera. A maximum error of 0.3 °C is reported between thermal camera and FPGA measurements.

A Molecular Imprinted PEDOT CMOS Chip-Based Biosensor for Carbamazepine Detection.

A miniaturized biosensor for carbamazepine (CBZ) detection and quantification was designed, implemented and fabricated. The 1×1 mm2 CMOS chip was packaged and coupled with a 3-electrode electrochemical cell. A complete characterization of the sensor was conducted via two steps: 1) Molecular imprinting of PEDOT polymer sites by cyclic voltammetry (CV) on glassy carbon electrode (GCE) surfaces; and 2) Quantification of CBZ solutions through both CV, and a current peak detection circuitry. The proposed biosensor offered high-selectivity and high-sensitivity to CBZ molecules. Scanning electron microscopy (SEM) was utilized to validate the synthesis of the PEDOT chains. CBZ removal from the imprinted polymer was conducted through soaking the modified GCEs in acetonitrile (ACN). Extraction was then confirmed by ultraviolet-visible (UV-vis) spectroscopy and CV analyzing data from pre- and post-template extraction. Furthermore, in order to characterize the electrodes' response to CBZ levels in phosphate buffered solution (PBS) with [Fe(CN)6]3-/4- as a redox pair/mediator, CV and peak detection was conducted resulting in redox peak currents vs. CBZ concentration graphs. The limits of detection (LOD) and quantification (LOQ) were calculated to be 2.04 and 6.2 µg/mL respectively. Finally, selectivity towards CBZ was validated by studying the effect of valproic acid (VPA) and phenytoin (PHT) on the biosensor's performance. The proposed biosensor is highly sensitive and selective to CBZ molecules, simple to construct and easy to operate.

Contact and Remote Breathing Rate Monitoring Techniques: A Review.

Breathing rate monitoring is a must for hospitalized patients with the current coronavirus disease 2019 (COVID-19). We review in this paper recent implementations of breathing monitoring techniques, where both contact and remote approaches are presented. It is known that with non-contact monitoring, the patient is not tied to an instrument, which improves patients' comfort and enhances the accuracy of extracted breathing activity, since the distress generated by a contact device is avoided. Remote breathing monitoring allows screening people infected with COVID-19 by detecting abnormal respiratory patterns. However, non-contact methods show some disadvantages such as the higher set-up complexity compared to contact ones. On the other hand, many reported contact methods are mainly implemented using discrete components. While, numerous integrated solutions have been reported for non-contact techniques, such as continuous wave (CW) Doppler radar and ultrawideband (UWB) pulsed radar. These radar chips are discussed and their measured performances are summarized and compared.

A Real-Time Thermal Monitoring System Intended for Embedded Sensors Interfaces.

This paper proposes a real-time thermal monitoring method using embedded integrated sensor interfaces dedicated to industrial integrated system applications. Industrial sensor interfaces are complex systems that involve analog and mixed signals, where several parameters can influence their performance. These include the presence of heat sources near sensitive integrated circuits, and various heat transfer phenomena need to be considered. This creates a need for real-time thermal monitoring and management. Indeed, the control of transient temperature gradients or temperature differential variations as well as the prediction of possible induced thermal shocks and stress at early design phases of advanced integrated circuits and systems are essential. This paper addresses the growing requirements of microelectronics applications in several areas that experience fast variations in high-power density and thermal gradient differences caused by the implementation of different systems on the same chip, such as the new-generation 5G circuits. To mitigate adverse thermal effects, a real-time prediction algorithm is proposed and validated using the MCUXpresso tool applied to a Freescale embedded sensor board to monitor and predict its temperature profile in real time by programming the embedded sensor into the FRDM-KL26Z board. Based on discrete temperature measurements, the embedded system is used to predict, in advance, overheating situations in the embedded integrated circuit (IC). These results confirm the peak detection capability of the proposed algorithm that satisfactorily predicts thermal peaks in the FRDM-KL26Z board as modeled with a finite element thermal analysis tool (the Numerical Integrated elements for System Analysis (NISA) tool), to gauge the level of local thermomechanical stresses that may be induced. In this paper, the FPGA implementation and comparison measurements are also presented. This work provides a solution to the thermal stresses and local system overheating that have been a major concern for integrated sensor interface designers when designing integrated circuits in various high-performance technologies or harsh environments.

A GaN-Based Wireless Monitoring System for High-Temperature Applications.

A fully-integrated data transmission system based on gallium nitride (GaN) high-electron-mobility transistor (HEMT) devices is proposed. This system targets high-temperature (HT) applications, especially those involving pressure and temperature sensors for aerospace in which the environmental temperature exceeds 350 °C. The presented system includes a front-end amplifying the sensed signal (gain of 50 V/V), followed by a novel analog-to-digital converter driving a modulator exploiting the load-shift keying technique. An oscillation frequency of 1.5 MHz is used to ensure a robust wireless transmission through metallic-based barriers. To retrieve the data, a new demodulator architecture based on digital circuits is proposed. A 1 V amplitude difference can be detected between a high-amplitude (data-on) and a low-amplitude (data-off) of the received modulated signal. Two high-voltage supply levels (+14 V and -14 V) are required to operate the circuits. The layout of the proposed system was completed in a chip occupying 10.8 mm2. The HT characterization and modeling of integrated GaN devices and passive components are performed to ensure the reliability of simulation results. The performance of the various proposed building blocks, as well as the whole system, have been validated by simulation over the projected wide operating temperature range (25-350 °C).

Maximizing Data Transmission Rate for Implantable Devices Over a Single Inductive Link: Methodological Review.

Due to the constantly growing geriatric population and the projected increase of the prevalence of chronic diseases that are refractory to drugs, implantable medical devices (IMDs) such as neurostimulators, endoscopic capsules, artificial retinal prostheses, and brain-machine interfaces are being developed. According to many business forecast firms, the IMD market is expected to grow and they are subject to much research aiming to overcome the numerous challenges of their development. One of these challenges consists of designing a wireless power and data transmission system that has high power efficiency, high data rates, low power consumption, and high robustness against noise. This is in addition to minimal design and implementation complexity. This manuscript concerns a comprehensive survey of the latest techniques used to power up and communicate between an external base station and an IMD. Patient safety considerations related to biological, physical, electromagnetic, and electromagnetic interference concerns for wireless IMDs are also explored. The simultaneous powering and data communication techniques using a single inductive link for both power transfer and bidirectional data communication, including the various data modulation/demodulation techniques, are also reviewed. This review will hopefully contribute to the persistent efforts to implement compact reliable IMDs while lowering their cost and upsurging their benefits.

A high-efficiency low-voltage CMOS rectifier for harvesting energy in implantable devices.

We present, in this paper, a new full-wave CMOS rectifier dedicated for wirelessly-powered low-voltage biomedical implants. It uses bootstrapped capacitors to reduce the effective threshold voltage of selected MOS switches. It achieves a significant increase in its overall power efficiency and low voltage-drop. Therefore, the rectifier is good for applications with low-voltage power supplies and large load current. The rectifier topology does not require complex circuit design. The highest voltages available in the circuit are used to drive the gates of selected transistors in order to reduce leakage current and to lower their channel on-resistance, while having high transconductance. The proposed rectifier was fabricated using the standard TSMC 0.18 µm CMOS process. When connected to a sinusoidal source of 3.3 V peak amplitude, it allows improving the overall power efficiency by 11% compared to the best recently published results given by a gate cross-coupled-based structure.

An integrated biosensor for the detection of bio-entities using magnetotactic bacteria and CMOS technology.

An integrated biosensor for the detection of micron-size biological entities using Magnetotactic Bacteria (MTB) being guided under the control of an external magnetic field of a few Gauss is briefly described. The proposed biosensor will be implemented onto a silicon substrate compatible with standard CMOS technologies. To validate the proposed concept, a microfluidic device and a microelectronic chip have been fabricated. Pairs of planar microelectrodes are patterned on the bottom of a microchannel and connected with the microelectronic chip. When a microbead pushed by a single MTB passes between a pair of microelectrodes, a variation in electrical impedance is measured by embedded detection circuits. The paper reviews the proposed microsystem. It emphasizes the sensing principle and describes the technique for implementing the microelectrodes. Finally, the paper reports preliminary experimental results obtained with an integrated circuit implementing the interfacing electronics that confirm the feasibility of turning distinct impedance levels into digital decisions that would reflect the presence of biological entities, especially bacteria or functional microbeads.