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Impedance Sensing in CMOS-Embedded Microfluidics Using BEOL Electrodes

Journal of Microelectromechanical Systems 2024年第1期33卷 110-117页

作者： Weng, Wei-Yang Chien, Jun-Chau Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan University of California at Berkeley Department of Electrical Engineering and Computer Sciences BerkeleyCA94720 United States

This paper describes a novel CMOS-embedded microfluidics platform featuring on-chip impedance-sensing electrodes. The platform employs a single-step wet etching process, removing the CMOS back-end-of-line (BEOL) routing metals, to create hollow fluidic channels that can be closely integrated with active circuits. We optimize the process parameters and improve the etch rate by 10Í through screening different metal etchants and applying hydraulic pressure to enhance the etchant byproduct diffusion rates. To integrate on-chip electrodes for impedance sensing, we explore various strategies and present 'via' electrodes that maintain their integrity in the etching process while preserving detection sensitivity. We also investigate the long-term reliability of the platform. Finally, we demonstrate the efficacy of impedance sensing using ionic solutions of varying strengths. [2023-0119] © 1992-2012 IEEE.

关键词： Microfluidics

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A novel IoT-based arrhythmia detection system with ECG signals using a hybrid convolutional neural network and neural architecture search network

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International Journal of Applied Decision Sciences 2024年第5期17卷 636-656页

作者： Şentürk, Ümit Gülen, Ceren Polat, Kemal Department of Computer Engineering Faculty of Engineering Abant Izzet Baysal University Bolu Turkey Department of Computer Engineering Education of Graduate Institute Abant Izzet Baysal University Bolu Turkey Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu Turkey

Electrocardiogram (ECG) signals are the most common tool to evaluate the heart’s function in cardiovascular diagnosis. Irregular heartbeats (arrhythmia) found in the ECG play an essential role in diagnosing cardiovascular diseases (CVD). In this paper, we proposed an arrhythmia classification method with the neural architecture search network (NASNet) model, an optimised version of the convolutional neural networks (2D-CNN) model, which performs very well in visual information analysis classification. We aimed to use the arrhythmia classification problem in IoT and mobile devices with the NASNet model with low parameter numbers and processing capacity without performance loss. 2D input data have been obtained by converting the classified heart rate signals in the datasets into image files. The 2D data obtained have been classified by machine learning, CNN, and NASNet models. As a result of classification, 2D CNN accuracy was 97.51%, and the NASNet model was 96.89% accuracy. As a result of arrhythmia classification, the accuracy rates of the NASNet and the 2D CNN models were close. In conclusion, the proposed IoT-based arrhythmia detection system with ECG signals using a hybrid CNN and NASNet is a promising tool for the early detection of arrhythmias. Furthermore, it could help to reduce the mortality associated with these potentially fatal conditions. © 2024 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

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Strain Evolution in SiGe Nanosheet Transistor Process Flow

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IEEE Transactions on Electron Devices 2024年第5期71卷 2907-2913页

作者： Chou, Hung-Chun Chou, Tao Chueh, Shee-Jier Jan, Sun-Rong Huang, Bo-Wei Tu, Chien-Te Liu, Yi-Chun Wang, Li-Kai Liu, C.W. National Taiwan University Graduate School of Advanced Technology Taipei106 Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taipei106 Taiwan Graduate Institute of Photonics and Optoelectronics National Taiwan University Taipei106 Taiwan Graduate Institute of Electronics Engineering The Graduate Institute of Photonics and Optoelectronics The Graduate School of Advanced Technology National Taiwan University Department of Electrical Engineering Taipei106 Taiwan

The step-by-step strain evolution in the channel during the SiGe nanosheet (NS) integration process flow for pFETs is demonstrated using finite element analysis (FEA). The effect of device dimensions and defective source/drain (S/D) is studied. After fin formation, the 0.77% compressive biaxial strain resulting from the lattice mismatch between Si0.8Ge0.2 and Si substrate is observed. However, the strainxx is gradually relaxed during the S/D recess and the inner spacer cavity formation. A large strain xx is obtained on the channel along the current direction after Si0.6Ge0.4 S/D regrowth, increasing from 0.21% to 1.50% for defect-free S/D epitaxy. The compressive strain along the channel remains similar for different shapes of the S/D regrowth, with small variations between every channel. Decreasing the NS width only leads to an insignificant increase in channel strainxx until the nanowire structure is formed. Nevertheless, the scaled body thickness can enhance the channel strainxx substantially with the 21.2% compressive strainxx increase from tbody = 5 nm to tbody = 1 nm. The defective S/D is also simulated with air gaps between the merged epitaxes, where compressive strain in the channel is totally relaxed and further turns into tensile-strained. The hole mobility is expected to have a 3.6× enhancement with 1.5% compressive strainxx. © 1963-2012 IEEE.

关键词： Substrates

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EfficientSpeech: An On-Device Text to Speech Model 48

EfficientSpeech: An On-Device Text to Speech Model

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Atienza, Rowel University of the Philippines Electrical and Electronics Engineering Institute Ai Graduate Program Philippines

ISBN: (纸本)9781728163277

State of the art (SOTA) neural text to speech (TTS) models can generate natural-sounding synthetic voices. These models are characterized by large memory footprints and substantial number of operations due to the long-standing focus on speech quality with cloud inference in mind. Neural TTS models are generally not designed to perform standalone speech syntheses on resource-constrained and no Internet access edge devices. In this work, an efficient neural TTS called EfficientSpeech that synthesizes speech on an ARM CPU in real-time is proposed. EfficientSpeech uses a shallow non-autoregressive pyramid-structure transformer forming a U-Network. EfficientSpeech has 266k parameters and consumes 90 MFLOPS only or about 1% of the size and amount of computation in modern compact models such as Mixer-TTS. EfficientSpeech achieves an average mel generation real-time factor of 104.3 on an RPi4. Human evaluation shows only a slight degradation in audio quality as compared to FastSpeech2. © 2023 IEEE.

关键词： CMOS FLOPS parameters RTF TTS

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Intrinsic Gate Capacitance of Ultrathin Body Nanosheets Considering Quantum Effects

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IEEE Transactions on Electron Devices 2024年第4期71卷 2271-2277页

作者： Yao, Ching-Wang Lee, Yu-Chieh Lin, Hsin-Cheng Chou, Tao Chung, Tsai-Yu Wang, Li-Kai Yang, Jen-Wei Jan, Sun-Rong Liu, C.W. National Taiwan University Graduate School of Advanced Technology Taipei106 Taiwan National Taiwan University Graduate Institute of Electronics Engineering Taipei106 Taiwan National Taiwan University Graduate Institute of Photonics and Optoelectronics Taipei106 Taiwan Graduate Institute of Electronics Engineering Graduate Institute of Photonics and Optoelectronics Graduate School of Advanced Technology National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan

Considering the wave function, the decreasing thickness of ultrathin body from 10 nm down to 2 nm increases the intrinsic gate capacitance since the centroid of carriers located at the center of the channel is extremely close to the channel/oxide interface in the ultrathin body. By the self-consistent Poisson-Schrodinger solver with 6-kp parameters calibrated with nonlocal empirical pseudo-potential method (NEPM), the centroids of wavefunctions can be used to understand the increment in intrinsic gate capacitance with decreasing body thickness. Note that the simulated channel is p-type GeSn with [Sn] ranging from 0% to 20%. Our simulation also shows that nearly ideal subthreshold swing (SS) can be achieved by ultrathin body nanosheets (NSs). Thus, the concept of intrinsic areal gate capacitance proportional to channel peripheral width has to be modified in the ultrathin body channel. © 1963-2012 IEEE.

关键词： Nanosheets

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Deep Learning Based Navigation System for Train Line Equipment Inspection Using Drones 9

Deep Learning Based Navigation System for Train Line Equipme...

引用

9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Hosono, Yuki Surantha, Nico Graduate School of Science and Engineering Tokyo City University Department of Electrical Electronics and Communication Engineering Tokyo Japan Tokyo City University Faculty of Science and Engineering Department of Electrical Electronics and Communication Engineering Tokyo Japan

ISBN: (纸本)9798331510732

Early damage detection and regular train overhead line equipment inspection are essential for safe and reliable train operation. Traditionally, these inspections have been conducted directly by power line engineers at night. However, such methods face challenges like a shrinking workforce and limited mobility. Attempts have been made to enhance mobility by installing cameras on test vehicles, yet the analysis of captured images still requires manual intervention. With the advancement of drones, high-resolution cameras, and deep learning technology, AI-based inspections using drones have become a promising solution. This study aims to develop an AI-based drone navigation system for automated inspection of train overhead line equipment, focusing on improving inspection efficiency and accuracy through model training for future drone deployment. In this study, a simulator was employed to generate images of overhead line facilities, and a machine learning model was trained using these images, based on the concepts of Dronet and FlowDronet from previous studies. The model's performance was evaluated using varying numbers of training images. A test dataset was created from images captured by a drone in flight and processed with optical flow techniques. The results revealed that the model trained with 22000 images achieved the highest performance, with an Average accuracy of 87.90%, a Collision Recall of 89.60%, and a Non-Collision Recall of 85.99%. These findings underscore the importance of larger datasets in enhancing model generalization capabilities and demonstrate the effectiveness of utilizing drones and AI for automated inspection of train overhead line equipment. © 2024 IEEE.

关键词： Optical flows

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Robust Structural Design and Fabrication of High-Efficiency Functional Metasurfaces

Robust Structural Design and Fabrication of High-Efficiency ...

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2024 Specialty Optical Fibers, SOF 2024

作者： Chen, Po-Jui Lu, Chia-Wei Wu, Chin-Chuan Hsieh, Wen-Chin Wu, Chung-Chih Graduate Institute of Electronics Engineering Graduate Institute of Photonics and Optoelectronics Department of Electrical Engineering National Taiwan University Taipei106 Taiwan

Incomplete etching of nanostructures would reduce transmission of metasurfaces by 10%-20%. By over-etching the nanostructures, transmission can reach a value similar to or even higher than the nanostructures being pre... 详细信息

关键词： Nanoclay

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Security of Cyber-Physical Systems Against Actuator Attacks through Cryptography 11

Security of Cyber-Physical Systems Against Actuator Attacks ...

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11th International Conference on Information Technology, ICIT 2023

作者： Oliveira, Samuel Leal, André B. Teixeira, Marcelo Lopes, Yuri K. Department of Exact and Technological Sciences AP Macapá Brazil Graduate Program in Electrical Engineering SC Joinville Brazil Graduate Program in Electrical Engineering Brazil

ISBN: (纸本)9798350320060

The connectivity demanded by Industry 4.0 poses new challenges for cyber-physical systems (CPS), notably bolstering resilience against cyberattacks. In this paper, the authors use the formalism of Discrete Event Systems (DES) to design an approach that aims to protect CPS against actuator attacks using event-based symmetric cryptography. In this approach, controllable events that are properly selected are encrypted before being transmitted from the controller to the plant, leading intruders to mistakenly infer the supervisor's control actions. Thus, attacks on actuators do not have the expected impact on the plant, preventing unsafe states from being reached. The proposed approach is applied to a case study from the literature, where the identification of the attack is done without causing any damage while maintaining the integrity of the system. © 2023 IEEE.

关键词： Cryptography

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Dynamic-HDC: A Two-Stage Dynamic Inference Framework for Brain-Inspired Hyperdimensional Computing

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IEEE Journal on Emerging and Selected Topics in Circuits and Systems 2023年第4期13卷 1125-1136页

作者： Chuang, Yu-Chuan Chang, Cheng-Yang Wu, An-Yeu National Taiwan University Graduate Institute of Electronics Engineering Taipei10617 Taiwan National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan

Brain-inspired hyperdimensional computing (HDC) has attracted attention due to its energy efficiency and noise resilience in various IoT applications. However, striking the right balance between accuracy and efficiency in HDC remains a challenge. Specifically, HDC represents data as high-dimensional vectors known as hypervectors (HVs), where each component of HVs can be a high-precision integer or a low-cost bipolar number (+1/-1). However, this choice presents HDC with a significant trade-off between accuracy and efficiency. To address this challenge, we propose a two-stage dynamic inference framework called Dynamic-HDC that offers IoT applications a more flexible solution rather than limiting them to choose between the two extreme options. Dynamic-HDC leverages the strategies of early exit and model parameter adaptation. Unlike prior works that use a single HDC model to classify all data, Dynamic-HDC employs a cascade of models for two-stage inference. The first stage involves a low-cost, low-precision bipolar model, while the second stage utilizes a high-cost, high-precision integer model. By doing so, Dynamic-HDC can save computational resources for easy samples by performing an early exit when the low-cost bipolar model exhibits high confidence in its classification. For difficult samples, the high-precision integer model is conditionally activated to achieve more accurate predictions. To further enhance the efficiency of Dynamic-HDC, we introduce dynamic dimension selection (DDS) and dynamic class selection (DCS). These techniques enable the framework to dynamically adapt the dimensions and the number of classes in the HDC model, further optimizing performance. We evaluate the effectiveness of Dynamic-HDC on three commonly used benchmarks in HDC research, namely MNIST, ISOLET, and UCIHAR. Our simulation results demonstrate that Dynamic-HDC with different configurations can reduce energy consumption by 19.8-51.1% and execution time by 22.5-49.9% with negligible

关键词： Brain

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Modeling of Electrostatics and Currents in a Forward-Biased p-n Junction

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IEEE Transactions on Electron Devices 2024年第8期71卷 4489-4497页

作者： Lin, Kuan-Wun Taur, Yuan National Taiwan University Graduate Institute of Electronics Engineering Taipei10617 Taiwan University of California San Diego Department of Electrical and Computer Engineering La Jolla CA92093 United States

A general model is developed for forward-biased p-n junctions with arbitrary doping concentrations biased at any injection level. It goes beyond the commonly practiced depletion approximation, which is valid only under the low-level injection condition. It is shown that when the applied junction voltage exceeds the built-in potential, the profile of net charge density shrinks to two antisymmetric spikes, one going positive and one going negative, with the same magnitude given by the average of the n-type and p-type doping concentrations. It is also shown that under high-level injection condition, the drift current components become dominant, in some cases yielding an exp(qV/kT) dependence of the total current instead of the commonly perceived exp(qV/2kT) dependence (here, V is the intrinsic junction voltage). By equating the sum of drift and diffusion current densities on both sides of the junction, analytical expressions are derived for the electric fields and, therefore, the total currents. Finally, the decay lengths of excess carriers in the quasi-neutral regions are solved numerically from the current continuity equations coupled to Poisson's equation with no assumptions of charge neutrality. It is shown that the decay lengths in the high-level injection regime are doping-dependent and are longer than the well-known minority-carrier diffusion lengths in the low-level injection regime. © 1963-2012 IEEE.

关键词： Electric fields

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