Ultrasound-on-chip platform for medical imaging, analysis, and collective intelligence.

Over the past half-century, ultrasound imaging has become a key technology for assessing an ever-widening range of medical conditions at all stages of life. Despite ultrasound's proven value, expensive systems that require domain expertise in image acquisition and interpretation have limited its broad adoption. The proliferation of portable and low-cost ultrasound imaging can improve global health and also enable broad clinical and academic studies with great impact on the fields of medicine. Here, we describe the design of a complete ultrasound-on-chip, the first to be cleared by the Food and Drug Administration for 13 indications, comprising a two-dimensional array of silicon-based microelectromechanical systems (MEMS) ultrasonic sensors directly integrated into complementary metal-oxide-semiconductor-based control and processing electronics to enable an inexpensive whole-body imaging probe. The fabrication and design of the transducer array with on-chip analog and digital circuits, having an operating power consumption of 3 W or less, are described, in which approximately 9,000 seven-level feedback-based pulsers are individually addressable to each MEMS element and more than 11,000 amplifiers, more than 1,100 analog-to-digital converters, and more than 1 trillion operations per second are implemented. We quantify the measured performance and the ability to image areas of the body that traditionally takes three separate probes. Additionally, two applications of this platform are described-augmented reality assistance that guides the user in the acquisition of diagnostic-quality images of the heart and algorithms that automate the measurement of cardiac ejection fraction, an indicator of heart health.

Pre-enrichment-free detection of hepatocellular carcinoma-specific ctDNA via PDMS and MEMS-based microfluidic sensor.

The growing interest in microfluidic biosensors has led to improvements in the analytical performance of various sensing mechanisms. Although various sensors can be integrated with microfluidics, electrochemical ones have been most commonly employed due to their ease of miniaturization, integration ability, and low cost, making them an established point-of-care diagnostic method. This concept can be easily adapted to the detection of biomarkers specific to certain cancer types. Pathological profiling of hepatocellular carcinoma (HCC) is heterogeneous and rather complex, and biopsy samples contain limited information regarding the tumor and do not reflect its heterogeneity. Circulating tumor DNAs (ctDNAs), which can contain information regarding cancer characteristics, have been studied tremendously since liquid biopsy emerged as a new diagnostic method. Recent improvements in the accuracy and sensitivity of ctDNA determination also paved the way for genotyping of somatic genomic alterations. In this study, three-electrode (Au-Pt-Ag) glass chips were fabricated and combined with polydimethylsiloxane (PDMS) microchannels to establish an electrochemical microfluidic sensor for detecting c.747G > T hotspot mutations in the TP53 gene of ctDNAs from HCC. The preparation and analysis times of the constructed sensor were as short as 2 h in total, and a relatively high flow rate of 30 µl/min was used during immobilization and hybridization steps. To the best of our knowledge, this is the first time a PDMS-based microfluidic electrochemical sensor has been developed to target HCC ctDNAs. The system exhibited a limit of detection (LOD) of 24.1 fM within the tested range of 2-200 fM. The sensor demonstrated high specificity in tests conducted with fully noncomplementary and one-base mismatched target sequences. The developed platform is promising for detecting HCC-specific ctDNA at very low concentrations without requiring pre-enrichment steps.

Multisensor Integrated Platform Based on MEMS Charge Variation Sensing Technology for Biopotential Acquisition.

We propose a new methodology for long-term biopotential recording based on an MEMS multisensor integrated platform featuring a commercial electrostatic charge-transfer sensor. This family of sensors was originally intended for presence tracking in the automotive industry, so the existing setup was engineered for the acquisition of electrocardiograms, electroencephalograms, electrooculograms, and electromyography, designing a dedicated front-end and writing proper firmware for the specific application. Systematic tests on controls and nocturnal acquisitions from patients in a domestic environment will be discussed in detail. The excellent results indicate that this technology can provide a low-power, unexplored solution to biopotential acquisition. The technological breakthrough is in that it enables adding this type of functionality to existing MEMS boards at near-zero additional power consumption. For these reasons, it opens up additional possibilities for wearable sensors and strengthens the role of MEMS technology in medical wearables for the long-term synchronous acquisition of a wide range of signals.

A Hybrid Orbitrap-Nanoelectromechanical Systems Approach for the Analysis of Individual, Intact Proteins in Real Time.

Nanoelectromechanical systems (NEMS)-based mass spectrometry (MS) is an emerging technique that enables determination of the mass of individual adsorbed particles by driving nanomechanical devices at resonance and monitoring the real-time changes in their resonance frequencies induced by each single molecule adsorption event. We incorporate NEMS into an Orbitrap mass spectrometer and report our progress towards leveraging the single-molecule capabilities of the NEMS to enhance the dynamic range of conventional MS instrumentation and to offer new capabilities for performing deep proteomic analysis of clinically relevant samples. We use the hybrid instrument to deliver E. coli GroEL molecules (801 kDa) to the NEMS devices in their native, intact state. Custom ion optics are used to focus the beam down to 40 µm diameter with a maximum flux of 25 molecules/second. The mass spectrum obtained with NEMS-MS shows good agreement with the known mass of GroEL.

Mass-Fabrication Scheme of Highly Sensitive Wireless Electrodeless MEMS QCM Biosensor with Antennas on Inner Walls of Microchannel.

Quartz-crystal-microbalance (QCM) biosensor is a typical label-free biosensor, and its sensitivity can be greatly improved by removing electrodes and wires that would be otherwise attached to the surfaces of the quartz resonator. The wireless-electrodeless QCM biosensor was then developed using a microelectro-mechanical systems (MEMS) process, although challenges remain in the sensitivity, the coupling efficiency, and the miniaturization (or mass production). In this study, we establish a MEMS process to obtain a large number of identical ultrasensitive and highly efficient sensor chips with dimensions of 6 mm square. The fundamental shear resonance frequency of the thinned AT-cut quartz resonator packaged in the microchannel exceeds 160 MHz, which is excited by antennas deposited on inner walls of the microchannel, significantly improving the electro-mechanical coupling efficiency in the wireless operation. The high sensitivity of the developed MEMS QCM biosensors is confirmed by the immunoglobulin G (IgG) detection using protein A and ZZ-tag displaying a bionanocapsule (ZZ-BNC), where we find that the ZZ-BNC can provide more effective binding sites and higher affinity to the target molecules, indicating a further enhancement in the sensitivity of the MEMS QCM biosensor. We then perform the label-free C-reactive protein (CRP) detection using the ZZ-BNC-functionalized MEMS QCM biosensor, which achieves a detection limit of 1 ng mL-1 or less even with direct detection.

Natural Biopolymer-Based Biocompatible Conductors for Stretchable Bioelectronics.

Biocompatible conductors are important components for soft and stretchable bioelectronics for digital healthcare, which have attracted extensive research efforts. Natural biopolymers, compared to other polymers, possess unique features that make them promising building blocks for biocompatible conductors, such as good biocompatibility/biodegradability, natural abundance, sustainability, and capability, can be processed into various functional formats with tunable material properties under benign conditions. In this comprehensive review, we focus on the recent advances in biocompatible conductors based on natural biopolymers for stretchable bioelectronics. We first give a brief introduction of conductive components and natural polymers and summarize the recent development of biocompatible conductors based on representative natural biopolymers including protein (silk), polypeptide (gelatin), and polysaccharide (alginate). The design and fabrication strategies for biocompatible conductors based on these representative biopolymers are outlined, after the chemical structure and properties of such biopolymers are presented. Then we discuss the electronic component-biopolymer interface and bioelectronic-biological tissue (skin and internal tissues) interface, highlight various fabrication techniques of biocompatible conductors for soft bioelectronics, and introduce representative examples of utilizing natural biopolymer-based biocompatible conductors for on-skin bioelectronics, textile-based wearable electronics, and implantable bioelectronics for digital healthcare. Finally, we present concluding remarks on challenges and prospects for designing natural biopolymers for soft biocompatible conductors and bioelectronics.

A stochastic parabolic model of MEMS driven by fractional Brownian motion.

In this paper, we study a stochastic parabolic problem that emerges in the modeling and control of an electrically actuated MEMS (micro-electro-mechanical system) device. The dynamics under consideration are driven by an one dimensional fractional Brownian motion with Hurst index [Formula: see text]. We derive conditions under which the resulting SPDE has a global in time solution, and we provide analytic estimates for certain statistics of interest, such as quenching times and the corresponding quenching probabilities. Our results demonstrate the non-trivial impact of the fractional noise on the dynamics of the system. Given the significance of MEMS devices in biomedical applications, such as drug delivery and diagnostics, our results provide valuable insights into the reliability of these devices in the presence of positively correlated noise.

Biodegradable nanofiber-based piezoelectric transducer.

Piezoelectric materials, a type of "smart" material that generates electricity while deforming and vice versa, have been used extensively for many important implantable medical devices such as sensors, transducers, and actuators. However, commonly utilized piezoelectric materials are either toxic or nondegradable. Thus, implanted devices employing these materials raise a significant concern in terms of safety issues and often require an invasive removal surgery, which can damage directly interfaced tissues/organs. Here, we present a strategy for materials processing, device assembly, and electronic integration to 1) create biodegradable and biocompatible piezoelectric PLLA [poly(l-lactic acid)] nanofibers with a highly controllable, efficient, and stable piezoelectric performance, and 2) demonstrate device applications of this nanomaterial, including a highly sensitive biodegradable pressure sensor for monitoring vital physiological pressures and a biodegradable ultrasonic transducer for blood-brain barrier opening that can be used to facilitate the delivery of drugs into the brain. These significant applications, which have not been achieved so far by conventional piezoelectric materials and bulk piezoelectric PLLA, demonstrate the PLLA nanofibers as a powerful material platform that offers a profound impact on various medical fields including drug delivery, tissue engineering, and implanted medical devices.

Fabrication and Performance of a Ta2O5 Thin Film pH Sensor Manufactured Using MEMS Processes.

In this work, a capacitive pH sensor consisting of Ta2O5 functional film is designed and fabricated by employing MEMS-based procedures. The Ta2O5 thin film has an amorphous microstructure, and its surface roughness is less than 3.17 nm. A signal processing circuit and a software filtering algorithm are also designed to measure the pH value, thus improving the detection accuracy and anti-interference ability. Good linearity (R2 = 0.99904) and sensitivity (63.12 mV/pH) are recorded for the proposed sensing element in the range of pH 2~12. In addition, the sensor's drift and hysteresis are equal to 5.1 mV and 5.8 mV, respectively. The enhanced sensing performance in combination with the facile miniaturization process, low fabrication cost, and suitability for mass production render the fabricated sensor attractive for applications where pH change measurements in a water environment are required.

What MEMS Research and Development Can Learn from a Production Environment.

The intricate interdependency of device design and fabrication process complicates the development of microelectromechanical systems (MEMS). Commercial pressure has motivated industry to implement various tools and methods to overcome challenges and facilitate volume production. By now, these are only hesitantly being picked up and implemented in academic research. In this perspective, the applicability of these methods to research-focused MEMS development is investigated. It is found that even in the dynamics of a research endeavor, it is beneficial to adapt and apply tools and methods deduced from volume production. The key step is to change the perspective from fabricating devices to developing, maintaining and advancing the fabrication process. Tools and methods are introduced and discussed, using the development of magnetoelectric MEMS sensors within a collaborative research project as an illustrative example. This perspective provides both guidance to newcomers as well as inspiration to the well-versed experts.

Terahertz Detectors Using Microelectromechanical System Resonators.

The doubly clamped microelectromechanical system (MEMS) beam resonators exhibit extremely high sensitivity to tiny changes in the resonance frequency owing to their high quality (Q-) factors, even at room temperature. Such a sensitive frequency-shift scheme is very attractive for fast and highly sensitive terahertz (THz) detection. The MEMS resonator absorbs THz radiation and induces a temperature rise, leading to a shift in its resonance frequency. This frequency shift is proportional to the amount of THz radiation absorbed by the resonator and can be detected and quantified, thereby allowing the THz radiation to be measured. In this review, we present an overview of the THz bolometer based on the doubly clamped MEMS beam resonators in the aspects of working principle, readout, detection speed, sensitivity, and attempts at improving the performance. This allows one to have a comprehensive view of such a novel THz detector.

A Smart Active Phase-Change Micropump Based on CMOS-MEMS Technology.

The rational integration of many microfluidic chips and micropumps remains challenging. Due to the integration of the control system and sensors in active micropumps, they have unique advantages over passive micropumps when integrated into microfluidic chips. An active phase-change micropump based on complementary metal-oxide-semiconductor-microelectromechanical system (CMOS-MEMS) technology was fabricated and studied theoretically and experimentally. The micropump structure is simple and consists of a microchannel, a series of heater elements along the microchannel, an on-chip control system, and sensors. A simplified model was established to analyze the pumping effect of the traveling phase transition in the microchannel. The relationship between pumping conditions and flow rate was examined. Based on the experimental results, the maximum flow rate of the active phase-change micropump at room temperature is 22 µL/min, and long-term stable operation can be achieved by optimizing heating conditions.

How Good Is a Tactical-Grade GNSS + INS (MEMS and FOG) in a 20-m Bathymetric Survey?

This paper examines how tactical-grade Inertial Navigation Systems (INS), aided by Global Navigation Satellite System (GNSS) modules, vary from a survey-grade system in the bathymetric mapping in depths less than 20 m. The motivation stems from the advancements in sensor developments, measurement processing algorithms, and the proliferation of autonomous and uncrewed surface vehicles often seeking to use tactical-grade systems for high-quality bathymetric products. While the performance of survey-grade GNSS + INS is well-known to the hydrographic and marine science community, the performance and limitations of the tactical-grade micro-electro-mechanical system (MEMS) and tactical-grade fiber-optic-gyro (FOG) INS aided with GNSS require some study to answer the following questions: (1) How close or far is the tactical-grade GNSS + INS performance from the survey-grade systems? (2) For what survey order (IHO S-44 6th ed.) can a user deploy them? (3) Can we use them for navigation chart production? We attempt to answer these questions by deploying two tactical-grade GNSS + INS units (MEMS and FOG) and a survey-grade GNSS + INS on a survey boat. All systems collected data while operating a multibeam system with the lever-arm offsets accurately determined using a total station. The tactical-grade GNSS + INSs shared one pair of antennas for heading, while the survey-grade system used an independent antenna pair. We analyze the GNSS + INS results in sequence, examine the patch-test results, and the sensor-specific SBET-integrated bathymetric surfaces as metrics for determining the tactical-grade GNSS + INSs' reliability. In addition, we evaluate the multibeam's sounding uncertainties at different beam angles. The bathymetric surfaces using the tactical-grade navigation solutions are within 15 cm of the surface generated with the survey-grade solutions.

Real-Time Risk Assessment Detection for Weak People by Parallel Training Logical Execution of a Supervised Learning System Based on an IoT Wearable MEMS Accelerometer.

Activity monitoring has become a necessary demand for weak people to guarantee their safety. The paper proposed a Parallel Training Logical Execution (PTLE) system using machine learning (ML) models on a microelectromechanical system (MEMS) accelerometer to detect coughs, falls, and other normal activities. When there are many categories, the ML prediction can be confused between these activities with each other. The PTLE system trains several models in parallel with more specific activity classes in each dataset. The shared tasks between parallel models relieve the complexity for a single one. There are six additional parameters for accelerometer characteristics, which were calculated from three axes accelerations as input features to improve the ML's consciousness. Once all models were trained, the system was ready to receive the input accelerations and activated the logical flow to manage link operation between these ML models for output predictions. Random Forest (RF) had the highest potential among the ML classification algorithms after the validation. In the experiment, the comparison between the PTLE model and the regular ML model were carried out with real-time data from an M5stickC wearable device on the user's chest to the trained models on PC. The result showed the advancement of the proposed method in term of precision, recall, F1-score with an overall accuracy of 98% in the real-time test. The accelerations from the wearable device were sent to ML models via Wi-Fi with Message Queue Telemetry Transport (MQTT) broker, and the activity predictions were transferred to the cloud for the family members or doctor care based on Internet of Things (IoT) communication.

Advances in Silicon-Based Integrated Lidar.

Silicon-based Lidar is an ideal way to reduce the volume of the Lidar and realize monolithic integration. It removes the moving parts in the conventional device and realizes solid-state beam steering. The advantages of low cost, small size, and high beam steering speed have attracted the attention of many researchers. In order to facilitate researchers to quickly understand the research progress and direction, this paper mainly describes the research progress of silicon-based integrated Lidar, including silicon-based optical phased array Lidar, silicon-based optical switch array Lidar, and continuous frequency-modulated wave Lidar. In addition, we also introduced the scanning modes and working principles of other kinds of Lidar, such as the Micro-Electro-Mechanical System, mechanical Lidar, etc., and analyzed the characteristics of the Lidars above. Finally, we summarized this paper and put forward the future expectations of silicon-based integrated Lidar.

Multiple-Degree-of-Freedom Modeling and Simulation for Seismic-Grade Sigma-Delta MEMS Capacitive Accelerometers.

The high-order mechanical resonances of the sensing element in a high-vacuum environment can significantly degrade the noise and distortion performance of seismic-grade sigma-delta MEMS capacitive accelerometers. However, the current modeling approach is unable to evaluate the effects of high-order mechanical resonances. This study proposes a novel multiple-degree-of-freedom (MDOF) model to evaluate the noise and distortion induced by high-order mechanical resonances. Firstly, the MDOF dynamic equations of the sensing element are derived using the principle of modal superposition and Lagrange's equations. Secondly, a fifth-order electromechanical sigma-delta system of the MEMS accelerometer is established in Simulink based on the dynamic equations of the sensing element. Then, the mechanism through which the high-order mechanical resonances degrade the noise and distortion performances is discovered by analyzing the simulated result. Finally, a noise and distortion suppression method is proposed based on the appropriate improvement in high-order natural frequency. The results show that the low-frequency noise drastically decreases from about -120.5 dB to -175.3 dB after the high-order natural frequency increases from about 130 kHz to 455 kHz. The harmonic distortion also reduces significantly.

A Novel Packaging of the MEMS Gas Sensors Used for Harsh Outdoor and Human Exhale Sampling Applications.

Dust or condensed water present in harsh outdoor or high-humidity human breath samples are one of the key sources that cause false detection in Micro Electro-Mechanical System (MEMS) gas sensors. This paper proposes a novel packaging mechanism for MEMS gas sensors that utilizes a self-anchoring mechanism to embed a hydrophobic polytetrafluoroethylene (PTFE) filter into the upper cover of the gas sensor packaging. This approach is distinct from the current method of external pasting. The proposed packaging mechanism is successfully demonstrated in this study. The test results indicate that the innovative packaging with the PTFE filter reduced the average response value of the sensor to the humidity range of 75~95% RH by 60.6% compared to the packaging without the PTFE filter. Additionally, the packaging passed the High-Accelerated Temperature and Humidity Stress (HAST) reliability test. With a similar sensing mechanism, the proposed packaging embedded with a PTFE filter can be further employed for the application of exhalation-related, such as coronavirus disease 2019 (COVID-19), breath screening.

Artificial Intelligence Based Approach for Classification of Human Activities Using MEMS Sensors Data.

The integration of Micro Electronic Mechanical Systems (MEMS) sensor technology in smartphones has greatly improved the capability for Human Activity Recognition (HAR). By utilizing Machine Learning (ML) techniques and data from these sensors, various human motion activities can be classified. This study performed experiments and compiled a large dataset of nine daily activities, including Laying Down, Stationary, Walking, Brisk Walking, Running, Stairs-Up, Stairs-Down, Squatting, and Cycling. Several ML models, such as Decision Tree Classifier, Random Forest Classifier, K Neighbors Classifier, Multinomial Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine, were trained on sensor data collected from accelerometer, gyroscope, and magnetometer embedded in smartphones and wearable devices. The highest test accuracy of 95% was achieved using the random forest algorithm. Additionally, a custom-built Bidirectional Long-Short-Term Memory (Bi-LSTM) model, a type of Recurrent Neural Network (RNN), was proposed and yielded an improved test accuracy of 98.1%. This approach differs from traditional algorithmic-based human activity detection used in current wearable technologies, resulting in improved accuracy.

Research on Dust Effect for MEMS Thermal Wind Sensors.

This communication investigated the dust effect on microelectromechanical system (MEMS) thermal wind sensors, with an aim to evaluate performance in practical applications. An equivalent circuit was established to analyze the temperature gradient influenced by dust accumulation on the sensor's surface. The finite element method (FEM) simulation was carried out to verify the proposed model using COMSOL Multiphysics software. In experiments, dust was accumulated on the sensor's surface by two different methods. The measured results indicated that the output voltage for the sensor with dust on its surface was a little smaller than that of the sensor without dust at the same wind speed, which can degrade the measurement sensitivity and accuracy. Compared to the sensor without dust, the average voltage was reduced by about 1.91% and 3.75% when the dustiness was 0.04 g/mL and 0.12 g/mL, respectively. The results can provide a reference for the actual application of thermal wind sensors in harsh environments.

Pilot Study: Magnetic Motion Analysis for Swallowing Detection Using MEMS Cantilever Actuators.

The swallowing process involves complex muscle coordination mechanisms. When alterations in such mechanisms are produced by neurological conditions or diseases, a swallowing disorder known as dysphagia occurs. The instrumental evaluation of dysphagia is currently performed by invasive and experience-dependent techniques. Otherwise, non-invasive magnetic methods have proven to be suitable for various biomedical applications and might also be applicable for an objective swallowing assessment. In this pilot study, we performed a novel approach for deglutition evaluation based on active magnetic motion sensing with permanent magnet cantilever actuators. During the intake of liquids with different consistency, we recorded magnetic signals of relative movements between a stationary sensor and a body-worn actuator on the cricoid cartilage. Our results indicate the detection capability of swallowing-related movements in terms of a characteristic pattern. Consequently, the proposed technique offers the potential for dysphagia screening and biofeedback-based therapies.