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In this study, we explore the rejuvenation of a Zener diode degraded by high electrical stress, leading to a leftward shift, and broadening of the Zener breakdown voltage knee, alongside a 57% reduction in forward current. We employed a non-thermal annealing method involving high-density electric pulses with short pulse width and low frequency. The annealing process took <30 s at near-ambient temperature. Raman spectroscopy supports the electrical characterization, showing enhancement in crystallinity to explain the restoration of the breakdown knee followed by improvement in forward current by ∼85%.

 

In in-sensor image preprocessing, the sensed image undergoes low level processing like denoising at the sensor end, similar to the retina of human eye. Optoelectronic synapse devices are potential contenders for this purpose, and subsequent applications in artificial neural networks (ANNs). The optoelectronic synapses can offer image pre-processing functionalities at the pixel itself—termed as in-pixel computing. Denoising is an important problem in image preprocessing and several approaches have been used to denoise the input images. While most of those approaches require external circuitry, others are efficient only when the noisy pixels have significantly lower intensity compared to the actual pattern pixels. In this work, we present the innate ability of an optoelectronic synapse array to perform denoising at the pixel itself once it is trained to memorize an image. The synapses consist of phototransistors with bilayer MoS₂channel and p-Si/PtTe₂buried gate electrode. Our 7 × 7 array shows excellent robustness to noise due to the interplay between long-term potentiation and short-term potentiation. This bio-inspired strategy enables denoising of noise with higher intensity than the memorized pattern, without the use of any external circuitry. Specifically, due to the ability of these synapses to respond distinctively to wavelengths from 300 nm in ultraviolet to 2 µm in infrared, the pixel array also denoises mixed-color interferences. The “self-denoising” capability of such an artificial visual array has the capacity to eliminate the need for raw data transmission and thus, reduce subsequent image processing steps for supervised learning.

 

Thermal annealing is commonly used in fabrication processing and/or performance enhancement of electronic and opto-electronic devices. In this study, we investigate an alternative approach, where high current density pulses are used instead of high temperature. The basic premise is that the electron wind force, resulting from the momentum loss of high-energy electrons at defect sites, is capable of mobilizing internal defects. The proposed technique is demonstrated on commercially available optoelectronic devices with two different initial conditions. The first study involved a thermally degraded edge-emitting laser diode. About 90% of the resulting increase in forward current was mitigated by the proposed annealing technique where very low duty cycle was used to suppress any temperature rise. The second study was more challenging, where a pristine vertical-cavity surface-emitting laser (VCSEL) was subjected to similar processing to see if the technique can enhance performance. Encouragingly, this treatment yielded a notable improvement of over 20% in the forward current. These findings underscore the potential of electropulsing as an efficient in-operando technique for damage recovery and performance enhancement in optoelectronic devices.

 

Infrared thermography is a non-destructive technique that can be exploited in many fields including polymer composite investigation. Based on emissivity and thermal diffusivity variation; components, defects, and curing state of the composite can be identified. However, manual processing of thermal images that may contain significant artifacts, is prone to erroneous component and property determination. In this study, thermal images of different graphite/graphene-based polymer composites fabricated by hand, planetary, and batch mixing techniques were analyzed through an automatic machine learning model. Filler size, shape, and location can be identified in polymer composites and thus, the dispersion of different samples was quantified with a resolution of ~ 20 µm despite having artifacts in the thermal image. Thermal diffusivity comparison of three mixing techniques was performed for 40% graphite in the elastomer. Batch mixing demonstrated superior dispersion than planetary and hand mixing as the dispersion index (DI) for batch mixing was 0.07 while planetary and hand mixing showed 0.0865 and 0.163 respectively. Curing was investigated for a polymer with different fillers (PDMS took 500 s while PDMS-Graphene and PDMS Graphite Powder took 800 s to cure), and a thermal characteristic curve was generated to compare the composite quality. Therefore, the above-mentioned methods with machine learning algorithms can be a great tool to analyze composite both quantitatively and qualitatively.

 

Previous research has indicated that liver androgen receptors may play a role in modulating disease. This study aims to investigate the pathophysiology of high-fat diet (HFD) induced dysglycemia in male and female liver androgen receptor knockout (LivARKO) mice. We performed metabolic tests on LivARKO female and male mice fed a HFD or a control diet (from Research Diets Inc.) during months 1 or 2 after starting the diet. Additionally, we performed Western blot and quantitative real-time PCR analysis on the livers of the mice to examine intermediates in the insulin signaling pathway. LivARKO-HFD female mice displayed no difference in glucose tolerance compared to female LivARKO-Control (Con) mice, whereas in wild-type female mice, HFD impaired glucose tolerance (IGT). Our data suggests that starting at 1 month, LivARKO may be protecting female mice from HFD-induced metabolic dysfunction. LivARKO-HFD female mice displayed significantly worse insulin sensitivity at 15 minutes compared to LivARKO-Con female mice, but, strangely, LivARKO-HFD female mice had significantly better insulin sensitivity at 60 and 90 minutes compared to LivARKO-Con female mice. Despite protecting against IGT, LivARKO did not protect against HFD-induced hyperinsulinemia in female mice. In contrast to females, male LivARKO-HFD mice displayed impaired glucose tolerance compared to male LivARKO-Con mice. Thus, LivARKO is not protective against HFD-induced glucose metabolic dysfunction in male mice. Lastly, LivARKO-HFD female mice maintained hepatic insulin sensitivity whereas LivARKO-HFD male mice displayed hepatic insulin resistance. These findings suggest that LivARKO delayed the onset of HFD-induced dysglycemia in female mice.

 

Embedded devices, constrained by limited memory and processors, require deep learning models to be tailored to their specifications. This research explores customized model architectures for classifying drainage crossing images. Building on the foundational ResNet-18, this paper aims to maximize prediction accuracy, reduce memory size, and minimize inference latency. Various configurations were systematically probed by leveraging hardware-aware neural architecture search, accumulating 1,717 experimental results over six benchmarking variants. The experimental data analysis, enhanced by nn-Meter, provided a comprehensive understanding of inference latency across four different predictors. Significantly, a Pareto front analysis with three objectives of accuracy, latency, and memory resulted in five non-dominated solutions. These standout models showcased efficiency while retaining accuracy, offering a compelling alternative to the conventional ResNet-18 when deployed in resource-constrained environments. The paper concludes by highlighting insights drawn from the results and suggesting avenues for future exploration.