Microsystem Technologies最新文献

This research paper explores the design and analysis of partially depleted silicon on insulator (PDSOI) MOSFET and fully depleted silicon on insulator (FDSOI) MOSFET. This paper presents a comprehensive analysis of both DC and RF parameters in PDSOI and FDSOI MOSFETs. The investigation involves varying surface silicon thickness, source/drain doping levels, gate metal work functions, box oxide thickness, gate oxide thickness, and channel length modulation. By studying these diverse device parameters, the paper aims to gain insights into the performance characteristics of PDSOI and FDSOI MOSFETs and their suitability for different applications in integrated circuits. The findings contribute to a better understanding of device optimization and guide future advancements in semiconductor technology. The SILVACO TCAD tool is utilized for all aspects of design and analysis in this study. A thorough investigation is conducted on the floating body and its associated kink effects in a PDSOI device.

Diminution of leakage current is essential for the semiconductor device operating in the nanometer regime. This paper aims to analyse the consequence of metal work function on drain current, including leakage current. Gate-induced drain leakage (GIDL) is one of the critical parameters, and it is explored separately from the drain current in the nano-scaled fin-structured field effect transistor (FinFET). The analytical model is established to observe the influence of work function on drain current as well as GIDL. This paper also discusses doping concentration, vertical and lateral electric fields, and surface potential to model GIDL. In band-to-band tunnelling, electrons tunnel into drain owing to vertical electric field. Henceforth, change of the vertical electric field with gate potential with donor concentration is also studied. The transfer characteristics and transconductance (left( {g_{m} } right)) of device are also observed. The Y-parameter is extracted from the drain current (left( {I_{d} } right)) which is based on LambertW function and (g_{m}). The model is examined for hafnium oxide (HfO₂) and silicon dioxide (SiO₂). The analytical model is validated by TCAD simulation.

Mosquito breeding primarily occurs in drainage systems, leading to diseases such as yellow fever, dengue fever, and malaria transmitted through mosquito bites. While various automatic cleaning technologies have been developed to reduce the need for manual cleaning in subterranean drainages, open-style drainages still rely on labor-intensive manual cleaning practices. This not only adversely affects the health of those involved but also contributes to the spread of hazardous diseases. This paper introduces a solution in the form of a smart drainage cleaning system comprising five key modules: a wheeled chassis, an onboard controller unit, a robotic arm cleaning unit, a chemical spray unit, and a garage unit. The system is specifically designed for open-type drainages and operates along rails installed on the drainage side walls. It conducts daily cleaning routines, applying chemicals within the drainage to control mosquito populations. The garage unit serves as a secure storage location for the smart drainage module and facilitates battery recharging through a power charging station. The proposed system has been successfully implemented and tested across various drainage widths and depths in Kota Ramachandrapuram, Andhra Pradesh, India. It proves to be an effective tool for mosquito population control, ultimately safeguarding human lives from the dangerous diseases transmitted by mosquitoes.

Achieving higher luminous efficiency is a major concern in organic light emitting diodes (OLEDs). Diverse approaches, such as introduction of new material, altering device architecture, and implementing host–guest systems, are employed to attain higher luminous efficiency. The mechanism of photon transport inside different layers of various mediums and how it can affect or aid the luminous efficiency is not clearly investigated in many research studies undertaken so far. In this work, Monte Carlo simulation is used to understand the transport of a photon in three-layer OLED device model. Here, the impact of thicknesses of the electron transport layer (ETL), hole transport layer (HTL) and emissive layer (EML) on the photon transport is explored. It is observed that the percentage of photons absorbed, reflected and transmitted depends on the thickness of the layers above and beneath the EML. To have maximum transmittance at the anode end, the thickness of the EML, HTL and ETL layers are optimized as 30 nm, 35 nm and 40 nm respectively for this 3-layer OLED device.

The utilization of RF-MEMS, which stands for Microsystem-based (MEMS) Radio Frequency (RF) passive components, is garnering growing attention within the realm of Beyond-5G (B5G) and 6G technologies, despite its longstanding existence. This trend is fueled by the impressive RF characteristics achievable through the judicious exploitation of this technology. However, the complex interplay of various physical phenomena in RF-MEMS, spanning mechanical, electrical, and electromagnetic domains, renders the design and optimization of new configurations challenging. In this study, a modeling approach based on Lumped Element Networks (LEN) is employed to accurately predict the Scattering Parameters (S-parameters) characteristics of multi-state and highly reconfigurable RF-MEMS devices. The device under scrutiny is a multi-state RF step power attenuator, previously fabricated, tested, and documented in literature by the principal author. Although these physical devices exhibit flat attenuation characteristics, they are subject to certain non-idealities inherent to the technology. The refined LEN-based methodology presented herein aims to interpret and incorporate such undesirable parasitic effects to provide precise predictions for real RF-MEMS devices. Two custom metrics, referred to as Percent Magnitude Difference (PMD) and Percent Phase Difference (PPD), are utilized to evaluate the accuracy of the LEN model, revealing differences consistently within 1 and 8%, respectively, across a frequency range spanning from 100 MHz to 13.5 GHz.

Inerter, a mechanical two-terminal component that has force proportional to relative acceleration between its two terminals, has recently emerged as a promising suspension element to vehicle suspension systems. However, previous research studies have shown that the suspension improvement offered by a passive inerter is marginal. To address this limitation, this paper proposed a novel design of variable inerter, providing non-linear characteristic. However, the design of such a variable inerter poses challenges, specifically in determining unknown design parameters. With the goal of maximizing the suspension performance improvement, a multi-objective optimization approach is carried out to determine the optimal suspension performance improvement provided by a variable inerter based on quarter vehicle model. The optimization framework involves minimizing vehicle suspension performance criteria, such as vehicle body acceleration and dynamic tire load. Both aspects affect the ride comfort and road holding ability of a vehicle to ensure the passengers’ safety. The variable inerter is applied to both typical passenger car and heavy vehicle such as truck and the simulation result showed that a variable inerter outperforms passive inerter in both cases. Notably, the suspension performance improvement achieved in heavy vehicles is more substantial when compared to passenger cars. Therefore, the implementation of variable inerter in vehicle suspensions is proved to be beneficial.

Anomaly detection in textile images poses significant challenges due to the scarcity of defective samples and the complex nature of textile textures. This study presents a novel image processing workflow that enhances the unsupervised Variational Autoencoder’s (VAE) ability to identify anomalies in textile images, addressing the limitation of insufficient defective samples in real-world manufacturing scenarios. The primary motivation behind this research is to develop a robust anomaly detection method that can be trained using only normal samples, overcoming the common imbalance between normal and defective samples in the textile industry. Our proposed method introduces domain-specific techniques to preprocess images, assess the adequacy of training samples, and employ intuitive visual methods to differentiate between normal and abnormal samples. A key strength of our approach lies in strategically cropping original images into smaller blocks, increasing training samples and computational efficiency. However, this cropping step introduces abrupt boundary issues that can hinder accurate anomaly detection. To mitigate this problem, we developed a refined image processing approach that effectively resolves boundary artifacts, enabling precise localization of abnormal regions. We trained, tested, and validated our VAE model using the TILDA textile texture database. The experimental results highlight the robustness of our method, achieving high identification rates of 74% for normal samples and 76.9% for abnormal samples, even when trained solely on normal samples. The insights gained from this study have significant implications for the textile industry, paving the way for more efficient and reliable quality control processes.

In this manuscript, a numerical model based on the electric field, threshold voltage, sub-threshold current, and electrostatic potential in cylindrical coordinates using Poisson’s equation for triple hybrid metal (THM) gate dielectric modulated junctionless silicon-nanowire gate all around FET based uricase and ChO_X biosensor was developed at 40 nm technology (20 nm gate length) to study different gate engineering optimization effects on the performance of the proposed device. The results of the ATLAS-3D TCAD" device simulator agreed with a derived analytical model. Three types of gate optimization (gate engineering) are denoted by M_ϕ (4.86, 4.96 and 4.50 eV), O_ϕ (4.96, 4.86 and 4.50 eV), and Q_ϕ (4.86, 4.50 and 4.96 eV) each have three different metal work-function, including uricase and cholesterol oxidase (ChO_X) biomolecules have been coated in the nanocavity to determine their impact on the device performance and also, the effect of nanogap cavity length on the proposed device was examined taking numerous simulations. Our findings conclude that nanocavity coated with ChO_X dielectric and having tunable work-function optimized at “O” signifies better output results in the device sensitivity, shifting threshold voltage, switching ratio, transconductance, intrinsic voltage gain, and device efficiency. For instance, the switching ratio in the case of ChO_X biomolecule for M, O, and Q gate optimizations are 5.22 × 10⁵, 1.36 × 10⁶, and 2.18 × 10⁴, respectively. We conclude that the proposed devices with optimizing gate work function at “O” suggest new opportunities for future ultra-large-scale integration (ULSI) development to achieve highly efficient device performance.