Wireless breathable face mask sensor for spatiotemporal 2D respiration profiling and respiratory diagnosis.

Owing to air pollution and the pandemic outbreak, the need for quantitative pulmonary monitoring has greatly increased. The COVID-19 outbreak has aroused attention for comfortable wireless monitoring of respiratory profiles and more real-time diagnosis of respiratory diseases. Although respiration sensors have been investigated extensively with single-pixel sensors, 2D respiration profiling with a pixelated array sensor has not been demonstrated for both exhaling and inhaling. Since the pixelated array sensor allowed for simultaneous profiling of the nasal breathing and oral breathing, it provides essential respiratory information such as breathing patterns, respiration habit, breathing disorders. In this study, we introduced an air-permeable, stretchable, and a pixelated pressure sensor that can be integrated into a commercial face mask. The mask sensor showed a strain-independent pressure-sensing performance, providing 2D pressure profiles for exhalation and inhalation. Real-time 2D respiration profiles could monitor various respiratory behaviors, such as oral/nasal breathing, clogged nose, out-of-breath, and coughing. Furthermore, they could detect respiratory diseases, such as rhinitis, sleep apnea, and pneumonia. The 2D respiratory profiling mask sensor is expected to be employed for remote respiration monitoring and timely patient treatment.

Bimetallic Nanocatalysts Immobilized in Nanoporous Hydrogels for Long-Term Robust Continuous Glucose Monitoring of Smart Contact Lens.

Smart contact lenses for continuous glucose monitoring (CGM) have great potential for huge clinical impact. To date, their development has been limited by challenges in accurate detection of glucose without hysteresis for tear glucose monitoring to track the blood glucose levels. Here, long-term robust CGM in diabetic rabbits is demonstrated by using bimetallic nanocatalysts immobilized in nanoporous hydrogels in smart contact lenses. After redox reaction of glucose oxidase, the nanocatalysts facilitate rapid decomposition of hydrogen peroxide and nanoparticle-mediated charge transfer with drastically improved diffusion via rapid swelling of nanoporous hydrogels. The ocular glucose sensors result in high sensitivity, fast response time, low detection limit, low hysteresis, and rapid sensor warming-up time. In diabetic rabbits, smart contact lens can detect tear glucose levels consistent with blood glucose levels measured by a glucometer and a CGM device, reflecting rapid concentration changes without hysteresis. The CGM in a human demonstrates the feasibility of smart contact lenses for further clinical applications.

Smart Wireless Near-Infrared Light Emitting Contact Lens for the Treatment of Diabetic Retinopathy.

Diabetic retinopathy is currently treated by highly invasive repeated therapeutic injections and surgical interventions without complete vision recovery. Here, a noninvasive smart wireless far red/near-infrared (NIR) light emitting contact lens developed successfully for the repeated treatment of diabetic retinopathy with significantly improved compliance. A far red/NIR light emitting diode (LED) is connected with an application-specific integrated circuit chip, wireless power, and communication systems on a PET film, which is embedded in a silicone elastomer contact lens by thermal crosslinking. After in vitro characterization, it is confirmed that the retinal vascular hyper-permeability induced by diabetic retinopathy in rabbits is reduced to a statistically significant level by simply repeated wearing of smart far red/NIR LED contact lens for 8 weeks with 120 µW light irradiation for 15 min thrice a week. Histological analysis exhibits the safety and feasibility of LED contact lenses for treating diabetic retinopathy. This platform technology for smart LED contact lens would be harnessed for various biomedical photonic applications.

An 18.6-µW/Ch TDM-Based 8-Channel Noncontact ECG Recording IC with Common-Mode Interference Suppression.

This paper presents an 8-channel electrocardiogram (ECG) monitoring integrated circuit (IC) controlled by time-division multiplexing (TDM). The proposed TDM compensates the electrode DC offsets by forming an individual discrete-time feedback loop per channel while sharing an analog frontend. This enables a chopping-free open-loop amplification, achieving a high input impedance suitable for a noncontact ECG monitoring. In addition, a common-mode interference (CMI) cancellation scheme is also introduced in the proposed TDM schedule for the realization of a pseudo-driven-right leg (DRL) in a multichannel environment. The designed system is implemented in 180 nm CMOS. The chip dissipates 18.6 µW/channel including the power consumption by ADC. It shows the total-CMRR of 100 dB against CMI voltage swing up to 20 VPP. The chip is verified in noncontact 8-channel ECG using conventional passive electrodes.

Wireless smart contact lens for diabetic diagnosis and therapy.

A smart contact lens can be used as an excellent interface between the human body and an electronic device for wearable healthcare applications. Despite wide investigations of smart contact lenses for diagnostic applications, there has been no report on electrically controlled drug delivery in combination with real-time biometric analysis. Here, we developed smart contact lenses for both continuous glucose monitoring and treatment of diabetic retinopathy. The smart contact lens device, built on a biocompatible polymer, contains ultrathin, flexible electrical circuits and a microcontroller chip for real-time electrochemical biosensing, on-demand controlled drug delivery, wireless power management, and data communication. In diabetic rabbit models, we could measure tear glucose levels to be validated by the conventional invasive blood glucose tests and trigger drugs to be released from reservoirs for treating diabetic retinopathy. Together, we successfully demonstrated the feasibility of smart contact lenses for noninvasive and continuous diabetic diagnosis and diabetic retinopathy therapy.

Arrangement optimization of water-driven triboelectric nanogenerators considering capillary phenomenon between hydrophobic surfaces.

The rise in environmental issues has stimulated research on alternative energy. In this regard, triboelectric generation has received much attention as one of several new alternative energy sources. Among the triboelectric generation methods, solid-liquid triboelectric nanogenerators (SLTENGs) have been actively investigated owing to their durability and broad applicability. In this paper, we report on the optimum arrangement of SLTENGs to increase the generation of electrical energy. When hydrophobic SLTENGs are arranged in parallel with a specific intervening gap, the friction area between the water and the surface of the SLTENGs is changed owing to the different penetration distances of water between them. This difference affects the amount of triboelectricity generated; this change in the water contact area is caused by the capillary phenomenon. Therefore, we investigated the effect of the gap on water penetration and formulated an optimum arrangement to achieve optimum electricity generation efficiency when multiple SLTENGs are contained in a limited volume. The proposed optimum arrangement of SLTENGs is expected to have high utilization in energy harvesting from natural environment sources such as wave energy or water flow.

Hyaluronate-Gold Nanoparticle/Glucose Oxidase Complex for Highly Sensitive Wireless Noninvasive Glucose Sensors.

Noninvasive real-time biosensors to measure glucose levels in the body fluids have been widely investigated for continuous glucose monitoring of diabetic patients. However, they suffered from low sensitivity and reproducibility due to the instability of nanomaterials used for glucose biosensors. Here, we developed a hyaluronate-gold nanoparticle/glucose oxidase (HA-AuNP/GOx) complex and an ultralow-power application-specific integrated circuit chip for noninvasive and robust wireless patch-type glucose sensors. The HA-AuNP/GOx complex was prepared by the facile conjugation of thiolated HA to AuNPs and the following physical binding of GOx. The wireless glucose sensor exhibited slow water evaporation (0.11 µL/min), fast response (5 s), high sensitivity (12.37 µA·dL/mg·cm2) and selectivity, a low detection limit (0.5 mg/dL), and highly stable enzymatic activity (∼14 days). We successfully demonstrated the strong correlation between glucose concentrations measured by a commercially available blood glucometer and the wireless patch-type glucose sensor. Taken together, we could confirm the feasibility of the wireless patch-type robust glucose sensor for noninvasive and continuous diabetic diagnosis.

A Multilayer-Learning Current-Mode Neuromorphic System With Analog-Error Compensation.

Internet-of-things applications that use machine-learning algorithms have increased the demand for application-specific energy-efficient hardware that can perform both learning and inference tasks to adapt to endpoint users or environmental changes. This paper presents a multilayer-learning neuromorphic system with analog-based multiplier-accumulator (MAC), which can learn training data by stochastic gradient descent algorithm. As a component of the proposed system, a current-mode MAC processor, fabricated in 28-nm CMOS technology, performs both forward and backward processing in a crossbar structure of 500 × 500 6-b transposable SRAM arrays. The proposed system is verified in a two-layer neural network by using two prototype chips and an FPGA. Without any calibration circuit for the analog-based MAC, the proposed system compensates for non-idealities from analog operations by learning training data with the analog-based MAC. With 1-b (+1, 0, -1) batch update of 6-b synaptic weights, the proposed system achieves a recognition rate of 96.6% with a peak energy efficiency of 2.99 TOPS/W (1 OP = one unsigned 8-b × signed 6-b MAC operation) in the classification of the MNIST dataset.

Increased Interfacial Area between Dielectric Layer and Electrode of Triboelectric Nanogenerator toward Robustness and Boosted Energy Output.

Given the operation conditions wherein mechanical wear is inevitable, modifying bulk properties of the dielectric layer of a triboelectric nanogenerator (TENG) has been highlighted to boost its energy output. However, several concerns still remain in regards to the modification due to high-cost materials and cumbersome processes being required. Herein, we report TENG with a microstructured Al electrode (TENG_ME) as a new approach to modifying bulk properties of the dielectric layer. The microstructured Al electrode is utilized as a component of TENG to increase the interfacial area between the dielectric layer and electrode. Compared to the TENG with a flat Al electrode (TENG_F), the capacitance of TENG_ME is about 1.15 times higher than that of TENG_F, and the corresponding energy outputs of a TENG_ME are 117 µA and 71 V, each of which is over 1.2 times higher than that of the TENG_F. The robustness of TENG_ME is also confirmed in the measurement of energy outputs changing after sandpaper abrasion tests, repetitive contact, and separation (more than 105 cycles). The results imply that the robustness and long-lasting performance of the TENG_ME could be enough to apply in reliable auxiliary power sources for electronic devices.

A Spherical Hybrid Triboelectric Nanogenerator for Enhanced Water Wave Energy Harvesting.

Water waves are a continuously generated renewable source of energy. However, their random motion and low frequency pose significant challenges for harvesting their energy. Herein, we propose a spherical hybrid triboelectric nanogenerator (SH-TENG) that efficiently harvests the energy of low frequency, random water waves. The SH-TENG converts the kinetic energy of the water wave into solid⁻solid and solid⁻liquid triboelectric energy simultaneously using a single electrode. The electrical output of the SH-TENG for six degrees of freedom of motion in water was investigated. Further, in order to demonstrate hybrid energy harvesting from multiple energy sources using a single electrode on the SH-TENG, the charging performance of a capacitor was evaluated. The experimental results indicate that SH-TENGs have great potential for use in self-powered environmental monitoring systems that monitor factors such as water temperature, water wave height, and pollution levels in oceans.

An On-Chip Learning Neuromorphic Autoencoder With Current-Mode Transposable Memory Read and Virtual Lookup Table.

This paper presents an IC implementation of on-chip learning neuromorphic autoencoder unit in a form of rate-based spiking neural network. With a current-mode signaling scheme embedded in a 500 × 500 6b SRAM-based memory, the proposed architecture achieves simultaneous processing of multiplications and accumulations. In addition, a transposable memory read for both forward and backward propagations and a virtual lookup table are also proposed to perform an unsupervised learning of restricted Boltzmann machine. The IC is fabricated using 28-nm CMOS process and is verified in a three-layer network of encoder-decoder pair for training and recovery of images with two-dimensional pixels. With a dataset of 50 digits, the IC shows a normalized root mean square error of 0.078. Measured energy efficiencies are 4.46 pJ per synaptic operation for inference and 19.26 pJ per synaptic weight update for learning, respectively. The learning performance is also estimated by simulations if the proposed hardware architecture is extended to apply to a batch training of 60 000 MNIST datasets.

A Low-Power Wide Dynamic-Range Current Readout Circuit for Ion-Sensitive FET Sensors.

This paper presents an amplifier-less and digital-intensive current-to-digital converter for ion-sensitive FET sensors. Capacitance on the input node is utilized as a residue accumulator, and a clocked comparator is followed for quantization. Without any continuous-time feedback circuit, the converter performs a first-order noise shaping of the quantization error. In order to minimize static power consumption, the proposed circuit employs a single-ended current-steering digital-to-analog converter which flows only the same current as the input. By adopting a switching noise averaging algorithm, our dynamic element matching not only mitigates mismatch of current sources in the current-steering DAC, but also makes the effect of dynamic switching noise become an input-independent constant. The implemented circuit in 0.35 µm CMOS converts the current input with a range of 2.8 µ A to 15 b digital output in about 4 ms, showing a DNL of +0.24/-0.25 LSB and an INL of + 1.98/-1.98 LSB while consuming 16.8 µW.

A Single-Chip 64-Channel Ultrasound RX-Beamformer Including Analog Front-End and an LUT for Non-Uniform ADC-Sample-Clock Generation.

A 64-channel RX digital beamformer was implemented in a single chip for 3-D ultrasound medical imaging using 2-D phased-array transducers. The RX beamformer chip includes 64 analog front-end branches including 64 non-uniform sampling ADCs, a FIFO/Adder, and an on-chip look-up table (LUT). The LUT stores the information on the rising edge timing of the non-uniform ADC sampling clocks. To include the LUT inside the beamformer chip, the LUT size was reduced by around 240 times by approximating an ADC-sample-time profile w.r.t. focal points (FP) along a scanline (SL) for a channel into a piece-wise linear form. The maximum error between the approximated and accurate sample times of ADC is eight times the sample time resolution (Ts) that is 1/32 of the ultrasound signal period in this work. The non-uniform sampling reduces the FIFO size required for digital beamforming by around 20 times. By applying a 9-dot image from Field-II program and 2-D ultrasound phantom images to the fabricated RX beamformer chip, the original images were successfully reconstructed from the measured output. The chip in a 0.13-um CMOS occupies 30.25 [Formula: see text] and consumes 605 mW.

A single-chip 32-channel analog beamformer with 4-ns delay resolution and 768-ns maximum delay range for ultrasound medical imaging with a linear array transducer.

A single-chip 32-channel analog beamformer is proposed. It achieves a delay resolution of 4 ns and a maximum delay range of 768 ns. It has a focal-point based architecture, which consists of 7 sub-analog beamformers (sub-ABF). Each sub-ABF performs a RX focusing operation for a single focal point. Seven sub-ABFs perform a time-interleaving operation to achieve the maximum delay range of 768 ns. Phase interpolators are used in sub-ABFs to generate sampling clocks with the delay resolution of 4 ns from a low frequency system clock of 5 MHz. Each sub-ABF samples 32 echo signals at different times into sampling capacitors, which work as analog memory cells. The sampled 32 echo signals of each sub-ABF are originated from one target focal point at one instance. They are summed at one instance in a sub-ABF to perform the RX focusing for the target focal point. The proposed ABF chip has been fabricated in a 0.13- µ m CMOS process with an active area of 16 mm (2). The total power consumption is 287 mW. In measurement, the digital echo signals from a commercial ultrasound medical imaging machine were applied to the fabricated chip through commercial DAC chips. Due to the speed limitation of the DAC chips, the delay resolution was relaxed to 10 ns for the real-time measurement. A linear array transducer with no steering operation is used in this work.

An analog-digital hybrid RX beamformer chip with non-uniform sampling for ultrasound medical imaging with 2D CMUT array.

To reduce the memory area, a two-stage RX beamformer (BF) chip with 64 channels is proposed for the ultrasound medical imaging with a 2D CMUT array. The chip retrieved successfully two B-mode phantom images with a steering angle from -45 (°) to +45 (°), the maximum delay range of 8 µs, and the delay resolution of 6.25 ns. An analog-digital hybrid BF (HBF) is chosen for the proposed chip to utilize the easy beamforming operation in the digital domain and also to reduce chip area by minimizing the number of ADCs. The chip consists of eight analog beamformers (ABF) for the 1st-stage and a digital beamformer (DBF) for the 2nd-stage. The two-stage architecture reduces the memory area of both ABF and DBF by around four times. The DBF circuit is divided into three steps to further reduce the digital FIFO memory area by around twice. Coupled with the non-uniform sampling scheme, the proposed two-stage HBF chip reduces the total memory area by around 40 times compared to the uniform-sampling single-stage BF chip. The chip fabricated in a 0.13- µm CMOS process occupies the area of 19.4 mm(2), and dissipates 1.14 W with the analog supply of 3.3 V and the digital supply of 1.2 V.