Silicon carbide (SiC) devices are widely used in electric vehicles due to their low on-resistance and excellent thermal conductivity. In this study, we design a high-speed SiC gate driver chip using the TSMC$0.18\mu $m HV process for a half-bridge driver. Both high-side and low-side driving circuits incorporate overdriving techniques to reduce turn-on time. A new signal isolator is implemented to ensure isolation between the high-side and low-side circuits. Two driver chips, one for the high-side and one for the low-side, were successfully fabricated, occupying only 1.69 mm2of silicon area. For system integration, a microprocessor controls the gate driver chip, voltage converter, and SiC devices to drive a motor. A high-performance crosstalk suppression circuit is designed to enhance system efficiency. Experimental results show that the proposed circuit reduces crosstalk levels by 87% without requiring a filtering capacitor, and a negative voltage source.

This work presents a half-bridge gallium nitride (GaN) buck converter using an adaptive charge sharing bootstrap (ACSB) technique to stabilize the bootstrap capacitor voltage. The conventional bootstrap technique relies on preset calculation to decide the bootstrap capacitance. However, the PCB parasitic capacitance and Qgvariations compromise the results. Moreover, the fixed bootstrap capacitance limits the practicality in monolithic gate drivers and only few GaN transistors with certain gate charge (Qg) range can be used. The proposed ACSB improves the conduction loss and avoids the gate overstress in GaN devices. In addition, the charge sharing technique requires merely a small on-chip bootstrap capacitor, which eliminates the bulk volume of the off-chip capacitor. The prototype is simulated in TSMC T18HVG2 process. Simulation results show that when Qgranges from 115 pC to 585 pC, the bootstrap voltage steadily maintains at 4.7 V. The overshoot and undershoot are 16.2 mV and 19.6 mV, respectively when the load current transits between 2A and 500 mA. The peak power efficiency of 93.2 % is obtained.

A digitally controlled dual-mode buck converter using low quiescent current is proposed in this work. In particular, the proposed dual pulse-skipping mode adopts a pulse-skipping adaptive on-time (PSAOT) mode for heavy load; and a pulse-skipping asynchronous mode (PSAM) for light and ultra-light loads to achieve a 100,000X wide load range for the Long-range (LoRa) application. Specifically, the pulse-skipping mode selector adopts a digital logic circuit to realize the 180nA low quiescent current. In addition, a low-power dynamic comparator with an adjustable offset is used to accelerate the transient response. The proposed converter is fabricated in a TSMC 350 nm CMOS process and results in an output voltage of 3.3 V from 5V to 4.2 V input voltage range. These results show that the overall system achieves 92.9 % peak eficiency at 8 mA output load and a figure of merit (FoM) of 2.29.

This paper proposes a dual-mode digital-control buck converter, which provides a 100,000X wide load range for internet of things. The proposed dual pulse-skipping modes include a pulse-skipping adaptive on-time (PSAOT) mode in the heavy load and a pulse-skipping asynchronous mode (PSAM) in the light load. The controller and mode selector are implemented in digital circuits that consume only 40.5 nA quiescent current. The proposed load detector does not require any extra current sensor. Not only the switching loss but also the quiescent power dissipation is minimized. A low-power dynamic comparator with adjustable offset is proposed to suppress the overshoot in transient responses. The proposed converter is designed in TSMC 350 nm CMOS process. Simulation results show that the converter provides a 3.3V output voltage from a 3.8V to 5V input voltage in a load range of $1 \mu \mathrm{A}$ to 100 mA. A 94.7% peak efficiency at 20 mA output loading and a Figure of merit (FoM) of 0.48 are achieved.

This paper proposes a battery management system, including a fast battery charger, battery aging diagnosis, and charge estimation and balancing. The charger adopts a single-inductor single-input dual-output architecture to achieve charge balancing among battery cells. Interleaved pulse charging is proposed to reduce the charging time and slow down the aging process of batteries as well. This method also significantly suppresses the variations of the temperature of battery cells and is beneficial to the implementation of charge balancing. An artificial neural network is proposed to detect the state of health (SOH) of battery cells and improve the accuracy of the state of charge (SOC) estimation. The prototype is implemented in TSMC 0.35-$\mu \text{m}$process and TensorFlow tools are used. Measurement results show that the interleaved pulse charging reduces 30% variation of the battery temperature and saves 24% charging time when charging four battery cells concurrently. A mean absolute error of SOC estimation of 0.35% is achieved in this work.

A battery management system with aging detection based on artificial neural network (ANN) for the state of charge (SOC) balancing is proposed in this paper. The charger adopts a single-inductor multiple-output architecture to achieve charge balancing among different battery cells. In constant current mode, the pulse charging is utilized to improve the charging speed and slow down the aging rate. Moreover, an ANN is proposed to detect the state of health (SOH) of the battery cells and improve the accuracy of the SOC estimation. TSMC 0.35-pm process and TensorFlow are used for simulations. A 94% power efficiency of the charger is achieved. The active area of this design is 1.5 × 1.5 mm2. Experimental results show that 0.32% root-mean square errors for the SOC estimation is obtained.

A current-mode control Li-ion battery charger is proposed in this paper. The main architecture adopts two-loop current-mode control in the constant current (CC) and the constant voltage (CV) stages. Compare to the voltage-mode control, the proposed architecture reduces the complexity significantly. Trickle-current mode provides complete battery charging process to protect the battery. The built-in battery resistance detector is proposed to achieve aging detection while charging. The proposed charger is fabricated in TSMC 0.35-μm process, achieving 78% power efficiency.

A novel wind energy harvesting (WEH) system using micro-watt turbines is proposed in this paper. To extend the range of wind velocities the harvesting system can process and enhance the settling at the same time, we proposed to use the parasitic inductor of the wind turbines for local excitations. Two-stage power converter is utilized in this system, including a regulator and a maximum power extraction (MPE) implemented in the same chip. The smallest input voltage can be as low as 0.25 V and the settling time is less than 0.2 s with a 1 mF rectified capacitor. This work is fabricated in TSMC 0.35-μm process, achieving 63% end-to-end efficiency. The total active area is 7.68 mm2.

In this paper, a 10b 100-to-500 kS/s asynchronous SAR ADC is proposed and prototyped in 0.18 μm CMOS. The supply voltage is scaled down appropriately for different sampling speeds to minimize the power consumption. At a 0.5-V supply voltage and a 100 kS/s sampling rate, the ADC achieves a signal-to-noise and distortion ratio of 56.35 dB and consumes 424 nW, resultin g in a figure of merit of 7.9 fJ/conversion-step. The ADC core occupies an active area of only 0.077 mm2.

Liquid (or gas) chromatography coupled with mass spectrometry (LC/GC-MS) allows quantitative comparison of biomolecular abundance in clinical samples to help with the discovery of candidate biomarkers for complex diseases such as cancer. A fundamental challenge in quantitation of biomolecules for cancer biomarker discovery is owing to the heterogeneous nature of clinical samples. Various contaminations from related disease tissues or adjacent non-cancerous constituents in a sample confound the characterization of molecular expression profiles and thus hinder the discovery of reliable biomarkers. This issue has been raised and discussed in analysis of microarray and RNA-seq data in cancer genomics studies. To the best of our knowledge, the issue has not yet been rigorously addressed in analyzing LC/GC-MS data that are generated in a variety of omic studies including proteomics and metabolomics. Purification of LC/GC-MS based biomolecular expression profiles is highly desired prior to subsequent analysis, e.g., quantitative comparison of the abundance of biomolecules in clinical samples. In this study, we applied a topic model to computationally deconvolute each of LC/GC-MS based cancer expression profiles and infer the underlying sample-specific pure cancer profiles. We demonstrated the capability of the model in capturing mixture proportions of contaminants and cancer profiles on a synthetic LC-MS dataset. Improved performances were also achieved on experimental LC-MS based serum proteomic and GC-MS based tissue metabolomic datasets acquired from patients with hepatocellular carcinoma (HCC).

A self-sustaining regulator harvesting from two energy sources, solar and radio-frequency energy, is designed. A charge pump utilizing charge transfer switch (CTS) with tunable voltages at the bottom of the pumping capacitors is proposed. Through a DC-DC switching converter with a digital pulse-width modulator, the output voltage is regulated at 1V. In the digital DC-DC switching converter, we propose to analyze the error signal by an oversampling analog-to-digital converter (ADC). The system is completely implemented and fully-integrated in a standard 0.18μm CMOS process, and the achieved maximum end-to-end conversion efficiency is about 62%.

MOTIVATION: Liquid chromatography-mass spectrometry (LC-MS) has been widely used for profiling expression levels of biomolecules in various '-omic' studies including proteomics, metabolomics and glycomics. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time (RT) alignment, which is required to ensure that ion intensity measurements among multiple LC-MS runs are comparable, is one of the most important yet challenging preprocessing steps. Current alignment approaches estimate RT variability using either single chromatograms or detected peaks, but do not simultaneously take into account the complementary information embedded in the entire LC-MS data. RESULTS: We propose a Bayesian alignment model for LC-MS data analysis. The alignment model provides estimates of the RT variability along with uncertainty measures. The model enables integration of multiple sources of information including internal standards and clustered chromatograms in a mathematically rigorous framework. We apply the model to LC-MS metabolomic, proteomic and glycomic data. The performance of the model is evaluated based on ground-truth data, by measuring correlation of variation, RT difference across runs and peak-matching performance. We demonstrate that Bayesian alignment model improves significantly the RT alignment performance through appropriate integration of relevant information. AVAILABILITY AND IMPLEMENTATION: MATLAB code, raw and preprocessed LC-MS data are available at http://omics.georgetown.edu/alignLCMS.html. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Bayesian alignment model (BAM) is proposed for alignment of liquid chromatography-mass spectrometry (LC-MS) data. BAM belongs to the category of profile-based approaches, which are composed of two major components: a prototype function and a set of mapping functions. Appropriate estimation of these functions is crucial for good alignment results. BAM uses Markov chain Monte Carlo (MCMC) methods to draw inference on the model parameters and improves on existing MCMC-based alignment methods through 1) the implementation of an efficient MCMC sampler and 2) an adaptive selection of knots. A block Metropolis-Hastings algorithm that mitigates the problem of the MCMC sampler getting stuck at local modes of the posterior distribution is used for the update of the mapping function coefficients. In addition, a stochastic search variable selection (SSVS) methodology is used to determine the number and positions of knots. We applied BAM to a simulated data set, an LC-MS proteomic data set, and two LC-MS metabolomic data sets, and compared its performance with the Bayesian hierarchical curve registration (BHCR) model, the dynamic time-warping (DTW) model, and the continuous profile model (CPM). The advantage of applying appropriate profile-based retention time correction prior to performing a feature-based approach is also demonstrated through the metabolomic data sets.

This paper presents low-power analog ICs for wireless ECG acquisition systems. Considering the power-efficient communication in the body sensor network, the required low-power analog ICs are developed for a healthcare system through miniaturization and system integration. To acquire the ECG signal, a low-power analog front-end system, including an ECG signal acquisition board, an on-chip low-pass filter, and an on-chip successive-approximation analog-to-digital converter for portable ECG detection devices is presented. A quadrature CMOS voltage-controlled oscillator and a 2.4 GHz direct-conversion transmitter with a power amplifier and upconversion mixer are also developed to transmit the ECG signal through wireless communication. In the receiver, a 2.4 GHz fully integrated CMOS RF front end with a low-noise amplifier, differential power splitter, and quadrature mixer based on current-reused folded architecture is proposed. The circuits have been implemented to meet the specifications of the IEEE 802.15.4 2.4 GHz standard. The low-power ICs of the wireless ECG acquisition systems have been fabricated using a 0.18 μm Taiwan Semiconductor Manufacturing Company (TSMC) CMOS standard process. The measured results on the human body reveal that ECG signals can be acquired effectively by the proposed low-power analog front-end ICs.

A Bayesian alignment model (BAM) is proposed for alignment of liquid chromatography-mass spectrometry (LC-MS) data. BAM is composed of two important components: prototype function and mapping function. Estimation of both functions is crucial for the alignment result. We use Markov chain Monte Carlo (MCMC) methods for inference of model parameters. To address the trapping effect in local modes, we propose a block Metropolis-Hastings algorithm that leads to better mixing behavior in updating the mapping function coefficients. We applied BAM to both simulated and real LC-MS datasets, and compared its performance with the Bayesian hierarchical curve registration model (BHCR). Performance evaluation on both simulated and real datasets shows satisfactory results in terms of correlation coefficients and ratio of overlapping peak areas.

A novel framework of a probabilistic mixture regression model (PMRM) is presented for alignment of liquid chromatography-mass spectrometry (LC-MS) data with respect to retention time (RT) points. The expectation maximization algorithm is used to estimate the joint parameters of spline-based mixture regression models and prior transformation density models. The latter accounts for the variability in RT points and peak intensities. The applicability of PMRM for alignment of LC-MS data is demonstrated through three data sets. The performance of PMRM is compared with other alignment approaches including dynamic time warping, correlation optimized warping, and continuous profile model in terms of coefficient variation of replicate LC-MS runs and accuracy in detecting differentially abundant peptides/proteins.

Bacterial meningitis is still a life-threatening disease, and early diagnosis of pathogen can be crucial to improving survival rate. Using the surface-enhanced Raman scattering (SERS) platform developed by our group, the pathogens can be differentiated on the basis of their SERS spectra which are believed to related to their surface chemical components. We collected the SERS spectra of ten pathogens: Streptococcus pneumoniae(Spn), Streptococcus agalactiae (group B streptococcus, GBS), Staphylococcus aureus (Sa), Pseudomonas aeruginosae (Psa), Acinetobacter baumannii (Ab), Klebsiella pneumoniae (Kp), Neisseria meningitidis (Nm), Listeria monocy-togenes (Lm), Haemophilus influenzae (Hi), and Escherichia coli (E. coli). These samples were obtained from patients in National Taiwan University Hospital, and were believed to represent the real diversity of clinical pathogens. Using the support vector machine (SVM) method, the classification accuracy can achieve around 88%. However, we noted that SVM cannot distinguish between [E. coli, Kp] and [Sa, Hi] due to the fact that the global features of these two groups of pathogens are very similar. We therefore incorporated a classification tree method that can focus on local differences in classification rules. This improved the accuracy to 90%. To get a better understanding of the SERS signals, we also compared several other classification methods. In addition, rule extraction method which attempts to explain why classifier fail or succeed is also discussed. Our preliminary results are interesting, encouraging, and await more thorough investigation.