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The fifth generation (5G) mobile communication systems employ technologies such as massive MIMO, millimeter-wave (mmWave), and ultra-dense networks to support higher transmission data rate and lower latency, thereby enabling commercial deployment. Furthermore, the forthcoming sixth generation (6G) networks will integrate terrestrial and non-terrestrial networks, aiming to achieve full spectrum, full applications, and global coverage. Whether in the context of 5G or 6G networks, base transceiver stations (BTS) require a substantial number of radio frequency (RF) transceiver chains and antenna array, particularly in mmWave frequency bands. It is known that bandpass RF filters between antenna elements and transceivers are key components for suppressing out-of-band spurs and interference. The single board seamless integration of transceivers and antennas has become a growing trend. It means there is no extra room for a large number of filters at mmWave bands, leading to the emergence of integrated designs that combine filtering circuitry with antennas, known as filtenna or filtering antenna. With illustrated examples, the design methodologies, operational principles, and implementation strategies of filtennas are reviewed in this paper.

A hybrid (i.e., physics-guided data-driven) feedforward tracking controller is proposed for systems with unmodeled linear or nonlinear dynamics. The proposed controller is based on the filtered basis functions (FBF) approach, and hence called a hybrid FBF controller. It formulates the feedforward control input to a system as a linear combination of a set of basis functions whose coefficients are selected to minimize tracking errors. To predict the system response and thereby the tracking errors, the basis functions are filtered using a combination of two linear models. The first model is physics-based and remains unaltered during the execution of the controller, while the second is data-driven and is continuously updated during the execution of the controller. To ensure its practicality and safe learning, the proposed hybrid FBF controller is equipped with the abilities to handle delays in data acquisition and to detect impending instability due to its inherent data-driven feedback loop. The effectiveness of the hybrid FBF controller is demonstrated via application to vibration compensation of a 3D printer with unmodeled linear and nonlinear dynamics. Thanks to the proposed hybrid FBF controller, the tracking accuracy of the 3D printer and the print quality are both significantly improved in experiments involving high-speed printing, compared to standard FBF controller that does not incorporate a data-driven model. Furthermore, the ability of the hybrid FBF controller to detect, and hence to potentially avoid, impending instability is demonstrated offline using data collected online from experiments.

This article demonstrates a planar dual-band array antenna for millimeter-wave applications, which has a left-hand circular polarization and a right-hand circular polarization for two operating bands, respectively. By using aperture-fed stacked-curls, the proposed antenna can attain broad axial ratio (AR) bandwidths in both K- and Ka-bands through a single port. Meanwhile, two stacked-curls are designed to rotate in different directions for achieving orthogonal circular polarizations in two bands, respectively. An $8\times 8$ stacked-curls array antenna is designed and manufactured for verifications. The measured 3 dB AR bandwidths are 11.59% (18.7-21 GHz) in the K-band and 9.93% (26.8-29.6 GHz) in the Ka-band, with reflection coefficient lower than −10 dB. The maximum gain in K- and Ka-bands are 18.59 dB and 18.13 dB, respectively. The proposed antenna can be used for developing compact satellite communication systems.

In this work, the significant lifetime improvement of negative bias thermal instability (NBTI) is demonstrated by the introduction of a thin SiN layer fabricated by plasma enhanced atomic layer deposition (PEALD) in stress memorization technique (SMT). The thin SiN film is deposited before the plasma enhanced chemical vapor deposition (PECVD) of SiN layer with a high tensile stress. It is revealed that the possible H2 escape accompanied with interface de-passivation can be effectively suppressed by this thin PEALD SiN layer, which may further reduce the interface states at Si/gate dielectric interface. Hence, about 500% NBTI lifetime improvement for PMOSFETs is demonstrated without obvious performance degradation for both NMOSFETs and PMOSFETs. [ABSTRACT FROM AUTHOR]

It is known that the determination of the good-dice-in-bad-neighborhoods (GDBNs) has been regarded as an effective technique to reduce the value of the defect parts per million (DPPM) by identifying and rejecting the suspicious dice even though they are good in testing. Instead of examining eight immediate neighbors in a small-sized $3\times 3$ window or exploiting simple linear regression, a large-sized window can be used to recognize the broad-sighted neighborhoods and accurately infer the suspiciousness level for any given die. In this paper, the artificial neural networks (ANN)-based method can be proposed to solve the GDBN identification. Furthermore, two enhanced techniques can be further presented to improve the inference accuracy of the original ANN-based method by considering the variation of the time-dependent wafer patterns and the wafer-to-wafer relationship between two adjacent wafers. After applying the two enhanced techniques, the business profits can be improved in the new ANN-based method. Various experiments on two datasets clearly reveal the superiority of the proposed ANN-based method over the other existing methods. In addition to the reduction of the DPPM value, the new ANN-based method can achieve the 1.5X–2X better reduction in the cost of the return merchandise authorization (RMA). On the other hand, the experimental results show that the similar result can also be obtained in the other lower-yield products. By using the new ANN-based method, the relationships on bad dice cross wafers can be captured and the highly-accurate inference results can be simultaneously maintained. [ABSTRACT FROM AUTHOR]

There are mainly two types of wafer map failure pattern recognition, i.e., traditional classification based and deep learning based approaches. Traditional classification usually needs feature engineering, and deep learning requires lower human intervention and feature engineering. The joint requirement is noise filtering and identical input dimension size. Feature engineering and dimension resizing are artificial work and vary from study to study. In our study, we proposed an image hashing based wafer map resizing and pattern information retrieval framework. Traditional classification methods and CNN are employed for the wafer map failure pattern recognition. The experiments show that the image hashing based framework is the more appropriate method when compared to feature extraction and image resizing methods. We also found that proper input data manipulation can result in acceptable performance whether the used method is traditional classification algorithms or CNN. [ABSTRACT FROM AUTHOR]