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This article investigated the impact of immersive virtual reality (VR) content, using 360-degree videos, in undergraduate education. To improve the delivery and reality of 360-degree VR content, we filmed the video in the third person so that the viewers could feel like they were in the environment where the lecture was conducted. To verify the educational effects, 33 university students participated in our experiment. We conducted pretest learning, using 360-degree videos, and posttest learning via conventional 2-D videos for statistical analysis. A paired t-test was used to compare the means of the pretest and the posttest. In addition, learning via 360-degree videos was assessed for its effectiveness through questionnaires consisting of five measurement elements--engagement, immersion, motivation, cognitive benefits, and perceived learning effectiveness--and comparing them to the existing 2-D video method, based on e-learning. From the results, we confirmed that the teaching material delivered through 360-degree VR content allows students to be more focused, immersed, and interested than 2-D learning modes. Furthermore, the high scores of cognitive and perceived learning elements imply that VR-based 360-degree educational content can encourage more active participation than traditional lectures and can improve the ability to analyze and organize study lessons.

This study implements a highly uniform 3D vertically stack resistive random‐access memory (VRRAM) with a four‐layer contact hole structure. The fabrication process of a four‐layer VRRAM is demonstrated, and its physical and electrical properties are thoroughly examined. X‐ray photoelectron spectroscopy and transmission electron microscopy are employed to analyze the chemical distribution and physical structure of the VRRAM device. Multilevel capability, reliable endurance (>104 cycles), and retention (104 s) are successfully obtained. Synaptic memory plasticity, such as spike time‐dependent plasticity, spike rate‐dependent plasticity, excitatory post‐synaptic current, paired‐pulse facilitation, and long‐term potentiation and depression is presented. Finally, the vector‐matrix multiplication (VMM) operation is conducted on a 4 × 12 VRRAM array, according to the low resistance state ratio. It is ascertained that the accuracy drop, which can occur due to VMM error, can be limited to a decrease of less than 0.44% point. Utilizing the high‐density, multilevel, and biological characteristics of VRRAM, it is possible to implement high‐performance neuromorphic systems that require densely integrated synaptic devices. [ABSTRACT FROM AUTHOR]

The detection of all the symbols transmitted simultaneously in multiuser systems using limited wireless resources is challenging. Traditional model‐based methods show high performance with perfect channel state information (CSI); however, severe performance degradation will occur if perfect CSI cannot be acquired. In contrast, data‐driven methods perform slightly worse than model‐based methods in terms of symbol error ratio performance in perfect CSI states; however, they are also able to overcome extreme performance degradation in imperfect CSI states. This study proposes a novel deep learning‐based method by improving a state‐of‐the‐art data‐driven technique called deep soft interference cancellation (DSIC). The enhanced DSIC (EDSIC) method detects multiuser symbols in a fully sequential manner and uses an efficient neural network structure to ensure high performance. Additionally, error‐propagation mitigation techniques are used to ensure robustness against channel uncertainty. The EDSIC guarantees a performance that is very close to the optimal performance of the existing model‐based methods in perfect CSI environments and the best performance in imperfect CSI environments. [ABSTRACT FROM AUTHOR]

This paper presents a physical space design method for mixed reality-based immersive environments by improving the color rendering of optically visible head-mounted displays. In OST-HMD, the phenomenon that the light of the external environment is combined with the enhanced image occurs, which reduces the visibility of the content. Moreover, previous studies to solve this problem required additional optical components or performed only simple verification. Proposed physical space was designed using black curtains, blinds, low-reflection structures, and adjustable lighting. The physical space can be adapted to the intended use of the physical space through physical transitions for multiple users without additional optics. To verify the usefulness of this environment, we measured the color gamut and intensity spectrum in three experimental environments. Also, we analyzed the factors that change the color gamut according to the components and the surrounding environment. As a result, the color gamut improved by up to 4.7 times from 19.92% to 93.53% based on the environment and improved by up to 4.1 times from 21% to 86.29% depending on the location. The experimental results revealed that the presented method could reduce the factors that degrade the color gamut within all measurement locations and significantly improve the color gamut. [ABSTRACT FROM AUTHOR]

In recent years, research on ferroelectric materials based on hafnium oxide has increased because of promising advantages such as fast operating speeds and CMOS process compatibility. In the case of Al-doped HfO2 (HAO), the remnant polarization (Pr), switching endurance, and high ON/OFF ratio can induce better ferroelectricity. In this work, three metal–ferroelectric–(insulator)–semiconductor MF(I)S devices with TiN/HAO/n+ Si and 1 nm and 2 nm thick SiO2 insulators inserted between the ferroelectric layer and the semiconductor have been studied. Doping Al2O3 results in enhanced ferroelectric properties such as switching voltage and higher polarization compared to that of undoped HfO2. It is because the stabilization of tetragonal phases results in a high dielectric constant. The MFIS (1 nm) device's high remnant polarization value of 37.8 μC cm−2 was measured using polarization-switching PUND (positive-up–negative-down). Additionally, with DC dual sweeping, I–V characteristics exhibited a wide memory window and a large tunneling electro-resistance (TER) ratio. Furthermore, it has shown improvement in current and energy performance because of the stronger bond between the ferroelectric layer and the bottom electrical material and high charge transfer efficiency. Finally, we have successfully demonstrated the properties of the interfacial SiO2 layer and found the thickness of the optimal interlayer for the MFIS structure. Various experiments were conducted to study the synaptic characteristics of FTJ devices, including the long-term potentiation and depression, paired-pulse facilitation (PPF), spike-timing-dependent plasticity (STDP), and the recognition and prediction ability of the device using reservoir computing (RC) technology. Through these experiments, the fabricated device is suitable as an ideal device for implementing energy-efficient and high-speed artificial neural network applications. [ABSTRACT FROM AUTHOR]

HfO2-based ferroelectric tunnel junctions (FTJs) are promising nonvolatile memory types for neural network applications because of their speed, low power, and excellent complementary metal-oxide semiconductor compatibility. Specifically, HfAlOx (HAO) has led to extensive research efforts owing to its outstanding ferroelectric performance. This is a result of the fact that the atomic radius of Al is smaller than that of Hf. In this study, we investigate the metal–ferroelectric–semiconductor device with an Al doping concentration of 2% that was annealed at 900 °C. A high-remnant polarization (Pr) value of 39.85 µC/cm2 and endurance were achieved by using the polarization switching positive-up-negative-down measurement method at this annealing condition. Our device shows long-term potentiation and depression properties, including high linearity and multiple conductance states for neuromorphic applications. Moreover, paired-pulse facilitation was implemented to mimic human synaptic functions. The construction of 16 states comprising four bits was achieved by employing reservoir computing with the FTJ device functioning as a physical reservoir. Finally, the results obtained from the experiment show promising outcomes for the ferroelectric memory characteristics and synaptic properties of the manufactured HAO device. [ABSTRACT FROM AUTHOR]

Since HfOx‐based ferroelectric tunnel junctions (FTJs) are attractive compared to perovskite‐based FTJs and other emerging memory devices, they are being actively studied recently. They have advantages such as a simple metal–insulator–metal structure, complementary metal oxide semiconductor (CMOS) compatibility, non‐destructive operation, and low power consumption. Moreover, doped HfOx‐based FTJs are in the spotlight in terms of neuromorphic engineering as a way of advancing from the von Neumann structure. In particular, Al dopant is effective for inducing ferroelectric properties due to its smaller radius than that of Hf. The optimal concentration of Al varies depending on the device materials and the annealing conditions during deposition. Therefore, in‐depth research is required for neuromorphic applications. Herein, the properties of FTJ devices according to Al doping concentrations are analyzed. Subsequently, using the device with the highest remanent polarization, neuromorphic applications are implemented, including spike‐timing‐dependent plasticity (STDP), paired‐pulse facilitation (PPF), long‐term potentiation, and depression. The characteristics in different frequency regions are also studied to satisfy the demand for fast switching. Finally, the FTJ device is used as a physical reservoir in reservoir computing for efficient processing of time‐dependent inputs. [ABSTRACT FROM AUTHOR]