A 640 nA IQ Output-Capacitor-Less Low Dropout (LDO) Regulator with Sub-Threshold Slew-Rate Enhancement for Narrow Band Internet of Things (NB-IoT) Applications.

An ultra-low quiescent current output-capacitor-less low dropout (OCL-LDO) regulator for power-sensitive applications is proposed in this paper. To improve the gain of the OCL-LDO feedback loop, the error amplifier employs a combination of a cross-coupled input stage for boosting the equivalent input transconductance and a negative resistance technique to improve the gain. Meanwhile, in order to address the issue of transient response of the ultra-low quiescent current OCL-LDO, a sub-threshold slew-rate enhancement circuit is proposed in this paper, which consists of a transient signal input stage and a slew-rate current increase branch. The proposed OCL-LDO is fabricated in a 0.18 µm CMOS process with an effective area of 0.049 mm2. According to the measurement results, the proposed OCL-LDO has a maximum load current of 100 mA and a minimum quiescent current of 640 nA at an input voltage of 1.2 V and an output voltage of 1 V. The overshoot and undershoot voltages are 197 mV and 201 mV, respectively, and the PSR of the OCL-LDO is -72.4 dB at 1 kHz when the load current is 100 µA. In addition, the OCL-LDO has a load regulation of 7.6 µV/mA and a line regulation of 0.87 mV/V.

PCB Defect Detection via Local Detail and Global Dependency Information.

Due to the impact of the production environment, there may be quality issues on the surface of printed circuit boards (PCBs), which could result in significant economic losses during the application process. As a result, PCB surface defect detection has become an essential step for managing PCB production quality. With the continuous advancement of PCB production technology, defects on PCBs now exhibit characteristics such as small areas and diverse styles. Utilizing global information plays a crucial role in detecting these small and variable defects. To address this challenge, we propose a novel defect detection framework named Defect Detection TRansformer (DDTR), which combines convolutional neural networks (CNNs) and transformer architectures. In the backbone, we employ the Residual Swin Transformer (ResSwinT) to extract both local detail information using ResNet and global dependency information through the Swin Transformer. This approach allows us to capture multi-scale features and enhance feature expression capabilities.In the neck of the network, we introduce spatial and channel multi-head self-attention (SCSA), enabling the network to focus on advantageous features in different dimensions. Moving to the head, we employ multiple cascaded detectors and classifiers to further improve defect detection accuracy. We conducted extensive experiments on the PKU-Market-PCB and DeepPCB datasets. Comparing our proposed DDTR framework with existing common methods, we achieved the highest F1-score and produced the most informative visualization results. Lastly, ablation experiments were performed to demonstrate the feasibility of individual modules within the DDTR framework. These experiments confirmed the effectiveness and contributions of our approach.

A Novel Bilinear Feature and Multi-Layer Fused Convolutional Neural Network for Tactile Shape Recognition.

Convolutional neural networks (CNNs) can automatically learn features from pressure information, and some studies have applied CNNs for tactile shape recognition. However, the limited density of the sensor and its flexibility requirement lead the obtained tactile images to have a low-resolution and blurred. To address this issue, we propose a bilinear feature and multi-layer fused convolutional neural network (BMF-CNN). The bilinear calculation of the feature improves the feature extraction capability of the network. Meanwhile, the multi-layer fusion strategy exploits the complementarity of different layers to enhance the feature utilization efficiency. To validate the proposed method, a 26 class letter-shape tactile image dataset with complex edges was constructed. The BMF-CNN model achieved a 98.64% average accuracy of tactile shape. The results show that BMF-CNN can deal with tactile shapes more effectively than traditional CNN and artificial feature methods.

Flexible pressure sensors with a highly pressure- and strain-sensitive layer based on nitroxyl radical-grafted hollow carbon spheres.

The spherical structure of hollow carbon spheres (HCSs) makes their contact resistance and tunnel resistance extremely sensitive to the distance between them, which can be used as a conductive filler for high-sensitivity pressure sensors. Compared with one- and two-dimensional carbon-based materials, HCSs require a higher filling concentration for constructing an effective conductive network due to their average conductivity, which affects the mechanical properties of the sensor. In a single-electron system, electrons are transferred by hopping between the nitroxyl radical monomers and when the distance between the monomers is shortened, the electron transfer rate of nitroxyl radical compounds can be increased, thus further improving their conductivity. In this work, a composite of nitroxyl radical-modified hollow carbon spheres (HCS-g-NO˙) and polydimethylsiloxane (PDMS) polymer is introduced, and the resistivity of HCS-g-NO˙ is about one magnitude lower than that of HCSs at the same filling concentration. A flexible piezoresistive sensor with HCS-g-NO˙@PDMS as the sensitive layer coated on the PET electrode is presented, in which the spacing between HCS-g-NO˙ changes, causing changes in the contact and tunnel resistances in the sensitive layer when mechanical stresses are applied. The sensor achieved a piezoresistive response of -0.55 kPa-1 and the tensile response of 211 , and a sensor array of nine pixels was successfully demonstrated; thus, it can be used as a high sensitivity pressure and strain sensor.