Microelectronic Engineering最新文献

The high energy density layered oxide LiNi_0.8Mn_0.1Co_0.1O₂ (NMC811) holds great promise as a cathode material for future Li-ion batteries. However, its application in electric vehicles is hindered by issues such as inadequate cycle performance and rate capability. Additionally, the corrosion caused by the electrolyte poses limitations on high voltage operation. In this study, Cerium Oxide (CeO₂) was used to coat NMC811 using wet chemical method followed by heat treatment. Distilled water was used to dissolve Ce salt instead of ethanol so that it can reduce coating costs and is more environmentally friendly. XRD analysis showed no significant change in the hexagonal crystal structure of NMC811 material but the appearance of small CeO₂ peaks in patterns. Electrochemical test of CeO₂ coated NMC811 exhibited 18% and 9% higher cyclic and rate performance, respectively in comparison to pristine material.

Pressure sensors are widely used in a variety of industrial automatic control environments and in everyday life, including production automatic control, aerospace, healthcare, electronic skin and many other industries. Different structural designs are suitable for different application scenarios. With the development of technology, the demand for high sensitivity and wide range pressure sensors is increasing. The appearance of graphene-based materials has pushed the performance of pressure sensors to new heights. In this paper, the research progress of pressure sensors in the past ten years based on graphene and its derivatives is deeply discussed. According to the classification of application directions based on different substrate structures, the current pressure sensors based on graphene and its derivatives are reviewed. Finally, the current development status of pressure sensing technology based on graphene and its derivatives is summarized, and the development prospect in this field is prospected.

Index Terms.

Pressure sensor, Graphene, Graphene derivatives.

The recent evolution of microelectromechanical systems (MEMSs) presents a more mature technology that expands from pure research towards multidisciplinary nanoelectromechanical systems (NEMS) research. The smaller size of NEMS makes them multifunctional, fast, energy-saving, and sensitive to any external stimuli. The extreme sensitivity of these NEMS opens new avenues to the various industrial sector of applications in biosensing, gas sensing, and medical implants which won't be possible with traditional MEMS counterparts. Most of the resistive-gas sensors are more popular than others but their elevated working temperatures consume more energy and limit their real-world applications. Various self-heating, embedded MEMS microheaters, and materials have been explored to improve the sensing performance. Thus, there is an urgent need of the hour to review the associated manufacturing techniques and evolution of MEMS fabrication for energy-saving gas sensors and new developments in this area. We overview the various manufacturing process and developments in MEMS/NEMS for gas sensor applications, and their historical perspectives, and provide future guidelines to meet the existing challenges for real-world gas sensing applications.

Memristive devices have emerged as promising alternatives to traditional complementary metal-oxide semiconductor (CMOS)-based circuits in the field of neuromorphic systems. These two-terminal electronic devices, known for their non-volatile memory properties, can emulate synaptic behavior within artificial neural networks, offering remarkable advantages, including scalability, energy efficiency, rapid operation, compact size, and ease of fabrication. They hold the potential to serve as fundamental components for artificial neurons, revolutionizing neuromorphic computing systems by closely mimicking biological neurons. However, the integration of resistive random-access memory (RRAM) into commercial production faces challenges due to substantial variations in resistive switching (RS) parameters, which include cycle-to-cycle (C2C) and device-to-device (D2D) fluctuations. These variations are rooted in the stochastic nature of RS, linked to physical mechanisms like diffusion and redox reactions. Nonetheless, limitations exist in the current analytical approaches, emphasizing the need for more standardized tools to assess memristive device reliability consistently. Weibull distribution is widely used to analyze RRAM variability and many further studies are based on it. However, this distribution may not work well for some memristive devices. In such cases, one can use other statistical distributions available in the literature. In the present work, statistical distributions, namely Weibull, Exponential, Log-Normal, Gamma, and Logistic distributions, are employed to scrutinize memristive devices device parameters, shedding light on their performance and reliability. Also, analytical methods namely maximum likelihood estimates for parameter estimation and Kolmogorov-Smirnov test for assessing goodness of fit of the distributions are used. This study aims to provide an approach with a deeper understanding of memristive device parameters and analysis techniques.

A Negative Capacitance Field effet transistor (NCFET) based Dual split control (DSC) 6T-SRAM cell has been designed and explored with Computing-in memory (CiM) architecture for energy efficient demonstration of Deep neural networks (DNN) basic operation such as Input-Weight (Dot) Product. The impact of ferro electric layer thickness (T_fe) on the SRAM cell perfomance metrics such as read noise margin (RNM), write noise margin (WNM) and energy efficiency for read and write operations have been analyzed at supply voltages of 0.3 V and 0.5 V. It has been observed that due to the steep slope characteristics, the NCFET based DSC 6T-SRAM cell design exhibits better RM, WM, and energy efficiency as compared to the baseline CMOS DSC SRAM cell design at V_DD = 0.3 V and 0.5 V respectively (with T_fe range of 1 nm to 3 nm). Further, NCFET dual split control scheme for 6T-SRAM cell demonstrate improved read stability and write ability when compared with NCFET 6 T-SRAM cell design along with improved energy efficiency. NCFET based DSC 6T-SRAM CiM cell design has ∼22.77× and 12.41× lower energy consumption compared to the équivalent baseline 40 nm CMOS/baseline SRAM CiM design and ∼ 25.80× and 22.76× lower energy consumption compared to the NCFET based SRAM CiM at V_DD = 0.3 V and 0.5 V respectively. NCFETs have improved steep subthreshold slope characteristics at an optimal T_fe value and NCFET SRAM based CiM circuits are expected to have higher noise margins and lower energy consumption compared to the baseline CMOS designs and are effective for NCFET based computing in-memory architectures with reduced read disturb issues in combination with DSC concept.

Hafnia or hafnium oxide is a high-$\kappa$ dielectric material with paramount importance in the realm of semiconductor devices. Recent advancements in 3D device structures require a few nanometer-thick conformal films on non-planar substrates. During the fabrication stage, the annealing process of thin films has been discovered to mitigate delamination issues at the film-substrate interface. However, it has been observed that the residual stress, which emerges as the film cools to room temperature, may lead to delamination. In this study, we propose an idealized atomistic model to mimic the critical region of a 3D-NAND structure, to get insights into the effect of thermal stress and delamination during the annealing of hafnia-made thin film. We employ molecular dynamics simulation using charge-optimized many-body potential (COMB) to perform heating and cooling simulations for different thicknesses of the hafnia layer. Our results suggest that, during heating, as the annealing temperature increases, the severity of delamination decreases. At extremely low thickness of the hafnia layer, delamination does not occur. However, significant delamination is observed during the cooling process, especially when the high temperature gradient is high.

In this work, a capacitor multiplier based on a Multiple Output-Voltage Difference Transconductance Amplifier (MO-VDTA) is built by using Arbel-Goldminz cells with extensive performance analysis. Considering the large chip area occupation of capacitors, capacitor multipliers are one of the most required analog building blocks in most of low frequency applications. In this respect, the obtained capacitor multiplier is tested in a 2nd order low-pass filter by changing the cut-off frequency from 2 kHz to around 12.4 kHz. The multiplication factor (denoted as “k”) of the proposed architecture can be adjusted electronically from 120 to 750 for approximately two decades, while the structure contains only a single active element with a base capacitance. Additionally, the multiplication factor can be safely increased by using additional transconductance stages in the MO-VDTA active block. In the performance analysis, post-layout results are provided in conjunction with process corners, Monte-Carlo analyses and experimental verifications on the basis of commercial off-the-shelf elements such as AD844 and LM13700s.

Medical images contain rich individual health information, making the protection of their privacy and security crucial. This study first proposes a novel multi-segment memristor based on a multi-segment linear function. Then, building upon the Sprott-B chaotic system, a mirror-symmetric memristor multi-scroll chaotic attractor (MMSCAs) is introduced by incorporating logic pulse signals and the novel multi-segment memristor. Dynamic analysis of MMSCAs is conducted from four aspects: equilibrium points, Lyapunov exponents and bifurcations, coexisting attractors, and complexity. Lyapunov exponents and bifurcation diagram analysis reveal rich dynamical behaviors in MMSCAs, including inverse period-doubling bifurcations, burst chaotic, transient chaotic, and offset boosting. MMSCAs exhibit periodic and chaotic attractors co-existing under different initial conditions, along with multi-stability and super multi-stability. Complexity analysis results indicate that MMSCAs possess higher complexity and better randomness compared to other memristor chaotic systems. The accuracy of the MMSCAs mathematical model is verified through circuit design and simulation, and the implementation of MMSCAs in the embedded domain is extended using the STM32 microcontroller. Finally, a new cryptographic system is designed by integrating MMSCAs with RNA computation and applied to medical image encryption. The security of the cryptographic system is evaluated through key space and sensitivity, histogram, and correlation, while its robustness is evaluated through resistance to cropping and noise. The analysis results demonstrate high security and strong robustness of the cryptographic system, offering a novel solution for the protection of medical image information.

The characterization of the self-heating effect (SHE) has been an important research topic in advanced technology, but the existing characterizations are few and the characterization process is relatively complex. In this research, a SHE characterization model is established based on the relationship between output transconductance variation ($∆g_m$), gate source voltage (V_GS) and temperature variation ($∆T$) caused by SHE through machine learning, and then the model is validated by theoretical analyses and experimental simulation. The characterization model is capable of directly calculating the $∆T$ caused by SHE during I - V testing and simplifying the SHE characterization steps while ensuring characterization accuracy ($∆T$ difference < 1 °C), thus saving costs. It is also found that the model can expand the characterization range (V_GS: 0.3–0.7 V) of SHE and conducts quantitative characterization with model calculation under different V_GS, realizing a high characterization resolution of V_GS: 0.01 V. The circuit level application proves that the method can be effectively applied to the characterization of the SHE and solves the problem of the characterization of the circuit level SHE.

We introduce a detailed design and fabrication process of Silicon microcantilever arrays for biomolecular detection in liquid environment, utilized with laser readout. We present typical fabrication problems and provide related solutions to obtain high quality resonators via a robust, reproducible and high-yield process. Sensors in these arrays are individually functionalized with self-assembled chemical monolayers exposing various pH-active end-groups into solution. Dynamic-mode controlled frequency measurements in varying pH solutions result in stress-induced change of the sensor spring constant. pH changes in the solution lead to deprotonation of exposed functional chemical groups at high pH and the repulsive charges induced strain is proportional to the quantity and confinement of charges at the sensor interface. These built-up strains that affect the mechanical stiffness can be reversibly relaxed when exposed again to low pH environments.