Rapidly Progressing Neurocognitive Disorder in a Male with FXTAS and Alzheimer's Disease.

Fragile X-associated tremor/ataxia syndrome (FXTAS) is a neurodegenerative disorder that usually begins in the early 60s and affects carriers of premutation expansion (55-200 CGG repeats) of the fragile X mental retardation 1 (FMR1) gene. Additional disorders can co-occur with FXTAS including Alzheimer's disease (AD). Here we discuss a case report of a male with 67 CGG repeats in FMR1 who had mild late-onset FXTAS symptoms followed by neurocognitive disorder symptoms consistent with AD. The patient has developed tremor and ataxia that are the two characteristic symptoms of FXTAS. In addition, he shows rapid cognitive decline, brain atrophy most substantial in the medial temporal lobe, and decreased metabolism in the brain regions that are the characteristic findings of AD. The purpose of this study is to describe a patient profile with both diseases and review the details of an overlap between these two diseases

Colour Reflectance Investigation of Decolourized Sulfur Dyed Cotton Knitted Fabric via Ozone Plasma Treatment

Ozone plasma treatment is accessible to be applied on shading adjustment and colour fading because of the capacity of ozone production. It is a green process that treats dyed cotton fabric under dry condition so as to avoid chemical pollutants. This study means to explore colour reflectance of decolourized sulfur dyed cotton texture using ozone plasma treatment. Sulfur dyed cotton textures with various colour depths (0.5%, 1.5%, 2.5%) were set up to be treated different plasma parameters, including ozone air concentrations (10%, 30%, 50%, 70%), water contents in terms of weight percentage (35%, 45%) of fabric and ozone air plasma treatment periods (10 mins, 20 mins, 30 mins). The colour fading result is assessed by the colour reflectance in percentage (R%) utilizing spectrophotometer under CIE standard illuminant D65. The valid colour fading based on high percentage of reflectance was demonstrated from plasma treatment under higher ozone air concentration (50% and 70% ozone in air) and longer time length of plasma treatment (20 mins and 30 mins). The level of water content contained in the cotton fabrics is appeared to have noteworthy relationship with the degree of decolourization

Vertical GaN diode BV maximization through rapid TCAD simulation and ML-enabled surrogate model

In this paper, two methodologies are used to speed up the maximization of the breakdown voltage (BV) of a vertical GaN diode that has a theoretical maximum BV of ∼ 2100 V. Firstly, we demonstrated a 5X faster accurate simulation method in Technology Computer-Aided-Design (TCAD). This allows us to find 50 % more numbers of high BV (\u3e1400 V) designs at a given simulation time. Secondly, a machine learning (ML) model is developed using TCAD-generated data and used as a surrogate model for differential evolution optimization. It can inversely design an out-of-the-training-range structure with BV as high as 1887 V (89 % of the ideal case) compared to ∼ 1100 V designed with human domain expertise

Vertical GaN Diode BV Maximization through Rapid TCAD Simulation and ML-enabled Surrogate Model

In this paper, two methodologies are used to speed up the maximization of the breakdown volt-age (BV) of a vertical GaN diode that has a theoretical maximum BV of ~2100V. Firstly, we demonstrated a 5X faster accurate simulation method in Technology Computer-Aided-Design (TCAD). This allows us to find 50% more numbers of high BV (>1400V) designs at a given simulation time. Secondly, a machine learning (ML) model is developed using TCAD-generated data and used as a surrogate model for differential evolution optimization. It can inversely design an out-of-the-training-range structure with BV as high as 1887V (89% of the ideal case) compared to ~1100V designed with human domain expertise.Comment: 4 pages, 7 figure

High-voltage vertical Ga\u3csub\u3e2\u3c/sub\u3eO\u3csub\u3e3\u3c/sub\u3e power rectifiers operational at high temperatures up to 600 K

This work presents the temperature-dependent forward conduction and reverse blocking characteristics of a high-voltage vertical Ga2O3 power rectifier from 300 K to 600 K. Vertical β-Ga2O3 Schottky barrier diodes (SBDs) were fabricated with a bevel-field-plated edge termination, where a beveled sidewall was implemented in both the mesa and the field plate oxide. The Schottky barrier height was found to increase from 1.2 eV to 1.3 eV as the temperature increases from 300 K to 600 K, indicating the existence of barrier height inhomogeneity. The net donor concentration in the drift region shows little dependence on the temperature. The reverse leakage current up to 500 V was found to be limited by both the thermionic-field electron injection at the Schottky contact and the electron hopping via the defect states in the depletion region. At 300-500 K, the leakage is first limited by the electron injection at low voltages and then by the hopping in depleted Ga2O3 at high voltages. At temperatures above 500 K, the thermionic field emission limits the device leakage over the entire voltage range up to 500 V. Compared to the state-of-the-art SiC and GaN SBDs when blocking a similar voltage, our vertical Ga2O3 SBDs are capable of operating at significantly higher temperatures and show a smaller leakage current increase with temperature. This shows the great potential of Ga2O3 SBDs for high-temperature and high-voltage power applications

Improvement of TCAD Augmented Machine Learning Using Autoencoder for Semiconductor Variation Identification and Inverse Design

A machine learning (ML) model by combing two autoencoders and one linear regression model is proposed to avoid overfitting and to improve the accuracy of Technology Computer-Aided Design (TCAD)-augmented ML for semiconductor structural variation identification and inverse design, without using domain expertise. TCAD-augmented ML utilizes TCAD simulations to generate sufficient data for ML model development when experimental data are inadequate. The ML model can then be used to identify semiconductor structural variation for given experimental electrical measurements. In this study, the variation of layer thicknesses in the p-i-n diode is used as a demonstration. An ML model is developed to predict the diode layer thicknesses based on a given Current-Voltage (IV) curve. Although the variations of interest can be incorporated easily in TCAD simulations to generate ML training data, the TCAD-augmented ML model generally is overfitted and cannot predict the variations in experiment well due to hidden variables which also alters the IV curves. We show that by using an autoencoder, this problem can be solved. To verify the effectiveness, another set of TCAD simulation data is generated with hidden variables (dopant concentration variation) to emulate experimental data. Testing on the second set of data shows that the proposed model can avoid overfitting and has up to 15 times improvement in accuracy in thickness prediction. Moreover, this model is used successfully to perform inverse design and can capture an underlying physics that cannot be described by a simple physical parameter

TCAD-Machine learning framework for device variation and operating temperature analysis with experimental demonstration

This work, for the first time, experimentally demonstrates a TCAD-Machine Learning (TCAD-ML) framework to assist the analysis of device-to-device variation and operating (ambient) temperature without the need of physical quantities extraction. The ML algorithm used in this work is the Principal Component Analysis (PCA) followed by third order polynomial regression. After calibrated to limited \u27expensive\u27 experimental data, \u27low cost\u27 TCAD simulation is used to generate a large amount of device data to train the ML model. The ML was then used to identify the root cause of device variation and operating temperature from any given experimental current-voltage (I-V) characteristics. We applied this framework to study the ultra-wide-bandgap gallium oxide (Ga2O3) Schottky barrier diode (SBD), an emerging device technology that holds great promise for temperature sensing, RF, and power applications in harsh environments. After calibration, over 150,000 electrothermal TCAD simulations are performed with random variation of physical parameters (anode effective work function, drift layer doping, and drift layer thickness) and operating temperature. An ML model is trained using these TCAD data and we found 1,000-10,000 TCAD data can train an accurate machine. We show that without physical quantities extraction, performing PCA is essential for the TCAD trained ML model to be applicable to analyze experimental characteristics. The physical parameters and temperatures predicted by the ML model show good agreement with experimental analysis. Our TCAD-ML framework shows great promise to accelerate the development of new device technologies with a significantly more efficient process of material and device experimentation

Study on function and appearance design of smart street lamps based on Kansei engineering: a literature review

The potential of smart cities to alleviate the challenges of urban development in relation to population, resources, and environment is widely recognized, making it a key urban development trend for the future. Smart street lamps (SSLs) are a crucial component of smart city infrastructure. However, their current unreasonable function settings and appearance design do not meet the emotional needs of residents and come at a high construction cost, resulting in decreased user satisfaction. Based on WOS and CNKI databases, 39 literatures on the aspects of theory, steps and technologies of KE, 32 literatures on the development, basic functions, construction, existing problems, and key technologies of SSLs, and 6 papers on street lamps functions or appearance design research based on KE be reviewed in this paper. Therefore, the application of KE method in SSL design be extensively reviewed, with emphasis on the future development direction of KE, the design principles of SSLs, and the implementation of KE in SSL design. This review aims to summarize the research gaps, future research directions, and future development trends of KE and SSL. Ultimately, the review concludes that the integration of KE in SSL design research is crucial to improve SSL products’ rationality, openness, and amicability, guided by scienti ic SSL design principles