Industry-Oriented Detection Method of PCBA Defects Using Semantic Segmentation Models

Automated optical inspection(AOI)is a significant process in printed circuit board assembly(PCBA)production lines which aims to detect tiny defects in PCBAs.Existing AOI equipment has several deficiencies including low throughput,large computation cost,high latency,and poor flexibility,which limits the efficiency of online PCBA inspection.In this paper,a novel PCBA defect detection method based on a lightweight deep convolution neural network is proposed.In this method,the semantic segmentation model is combined with a rule-based defect recognition algorithm to build up a defect detection frame-work.To improve the performance of the model,extensive real PCBA images are collected from production lines as datasets.Some optimization methods have been applied in the model according to production demand and enable integration in lightweight computing devices.Experiment results show that the production line using our method realizes a throughput more than three times higher than traditional methods.Our method can be integrated into a lightweight inference system and pro-mote the flexibility of AOI.The proposed method builds up a general paradigm and excellent example for model design and optimization oriented towards industrial requirements.

Oscillation neuron based on a low-variability threshold switching device for high-performance neuromorphic computing

Low-power and low-variability artificial neuronal devices are highly desired for high-performance neuromorphic computing.In this paper,an oscillation neuron based on a low-variability Ag nanodots(NDs)threshold switching(TS)device with low operation voltage,large on/off ratio and high uniformity is presented.Measurement results indicate that this neuron demonstrates self-oscillation behavior under applied voltages as low as 1 V.The oscillation frequency increases with the applied voltage pulse amplitude and decreases with the load resistance.It can then be used to evaluate the resistive random-access memory(RRAM)synaptic weights accurately when the oscillation neuron is connected to the output of the RRAM crossbar array for neuromorphic computing.Meanwhile,simulation results show that a large RRAM crossbar array(>128×128)can be supported by our oscillation neuron owing to the high on/off ratio(>10^(8))of Ag NDs TS device.Moreover,the high uniformity of the Ag NDs TS device helps improve the distribution of the output frequency and suppress the degradation of neural network recognition accuracy(<1%).Therefore,the developed oscillation neuron based on the Ag NDs TS device shows great potential for future neuromorphic computing applications.

Preface to the Special Issue on Beyond Moore:Three-Dimensional(3D)Heterogeneous Integration

In the past few decades,the Moore’s Law has been the revolutionary force for our integrated circuit(IC)industry.However,the tremendous challenges faced in continuous transistor physical down-scaling and the unprecedented demands for computing and storage capabilities require our urgent search for strategies and solutions to integrate diverse materials,devices,circuits,and architectures in a 3D vertically stacked manner so that they can orchestrate in the most effective way to provide significantly enhanced functionalities as well as superior speed,energy,bandwidth,form fact,and cost.

Preface to the Special Issue on Beyond Moore: Resistive Switching Devices for Emerging Memory and Neuromorphic Computing

Traditional charge-based memories,such as dynamic random-access memory(DRAM)and flash,are approaching their scaling limits.A variety of resistance-based memories,such as phase-change memory(PCM),magnetic random-access memory(MRAM)and resistive random-access memory(RRAM),have been long considered for emerging memory applications thanks to their non-volatility,fast speed,low power,and compact size for potentially high-density integration.

Effects of Injection of Oregano Oil Submicron Emulsion on Lipopolysaccharide-induced Pneumonia in Rats

[Objectives]To explore the effects of injection of oregano oil submicron emulsion on lipopolysaccharide-induced pneumonia in rats.[Methods]Rats induced by lipopolysaccharide were used as animal models of acute pneumonia.The experiment was divided into blank group,model group,administration group and positive drug control group.The morphology of lung tissue and the changes of cells and inflammatory factors in each group were observed,and the anti-inflammatory effects of injection of oregano oil submicron emulsion.[Results]The injection of oregano oil submicron emulsion can improve the pathological injury of rat lung tissue,inhibit the release of IL-6,IL-10,TNF-αcytokines induced by lipopolysaccharide,and significantly reduce the value of CRP.[Conclusions]Oregano oil submicron emulsion has a certain therapeutic effect on lipopolysaccharide-induced pneumonia in rats,and its mechanism may be related to reducing the release of cytoinflammatory factor IL-6,IL-10,and TNF-αand alleviating the injury to tissues and organs.

Memory materials and devices:From concept to application

Memory cells have always been an important element of information technology.With emerging technologies like big data and cloud computing,the scale and complexity of data storage has reached an unprecedented peak with a much higher requirement for memory technology.As is well known,better data storage is mostly achieved by miniaturization.However,as the size of the memory device is reduced,a series of problems,such as drain gate-induced leakage,greatly hinder the performance of memory units.To meet the increasing demands of information technology,novel and high-performance memory is urgently needed.Fortunately,emerging memory technologies are expected to improve memory performance and drive the information revolution.This review will focus on the progress of several emerging memory technologies,including two-dimensional material-based memories,resistance random access memory(RRAM),magnetic random access memory(MRAM),and phasechange random access memory(PCRAM).Advantages,mechanisms,and applications of these diverse memory technologies will be discussed in this review.

In situ optical backpropagation training of diffractive optical neural networks

Training an artificial neural network with backpropagation algorithms to perform advanced machine learning tasks requires an extensive computational process.This paper proposes to implement the backpropagation algorithm optically for in situ training of both linear and nonlinear diffractive optical neural networlks,which enables the acceleration of training speed and improvement in energy efficiency on core computing modules.We demonstrate that the gradient of a loss function with respect to the weights of diffractive layers can be accurately calculated by measuring the forward and backward propagated optical fields based on light reciprocity and phase conjunction principles.The diffractive modulation weights are updated by programming a high-speed spatial light modulator to minimize the error between prediction and target output and perform inference tasks at the speed of light.We numerically validate the effectiveness of our approach on simulated networks for various applications.The proposed in situ optical learning architecture achieves accuracy comparable to in silico training with an electronic computer on the tasks of object dlassification and matrix-vector multiplication,which further allows the diffractive optical neural network to adapt to system imperfections.Also,the self-adaptive property of our approach facilitates the novel application of the network for all-optical imaging through scattering media.The proposed approach paves the way for robust implementation of large-scale difractive neural networks to perform distinctive tasks all-optically.

Memristor-Based Signal Processing for Edge Computing

The rapid growth of the Internet of Things(IoTs)has resulted in an explosive increase in data,and thus has raised new challenges for data processing units.Edge computing,which settles signal processing and computing tasks at the edge of networks rather than uploading data to the cloud,can reduce the amount of data for transmission and is a promising solution to address the challenges.One of the potential candidates for edge computing is a memristor,an emerging nonvolatile memory device that has the capability of in-memory computing.In this article,from the perspective of edge computing,we review recent progress on memristor-based signal processing methods,especially on the aspects of signal preprocessing and feature extraction.Then,we describe memristor-based signal classification and regression,and end-to-end signal processing.In all these applications,memristors serve as critical accelerators to greatly improve the overall system performance,such as power efficiency and processing speed.Finally,we discuss existing challenges and future outlooks for memristor-based signal processing systems.

电导金属细丝主导的杂化钙钛矿基存储器（英文）

有机-无机杂化钙钛矿(OHPs)作为太阳能电池中的光吸收材料备受重视,并且在电阻开关(RS)存储器的应用中引起了广泛关注.以前的研究表明,在外电场作用下钙钛矿中的离子能够发生迁移并形成导电通道.然而,主导其阻变行为的是Ag还是卤素仍然存在着争议.本文中,我们研究了一种基于Ag/FA0.83MA0.17Pb(I0.82Br0.18)3/FT0(掺氟的氧化锡)的电阻开关存储器.在开启过程(在Ag电极端施加正向扫描电压)完成后,我们通过EDS(能最色散X射线谱)发现了银离子和卤素离子的迁移.并通过对比基于Au电极器件的电流-电压特征曲线,发现由Ag形成的导电通道是影响Ag基器件开关特性的主要因素.同时,通过控制合适大小的开启电压,基于Ag电极的电阻开关器件实现了模拟开关和阈值开关两种不同的阻变开关特性.因此,在未来有可能在单个器件中同时实现数据存储和神经形态计算两种功能.

In situ optical backpropagation training of diffractive optical neural networks:publisher’s note

This publisher’s note corrects the authors’affiliations in Photon.Res.8,940(2020).

Monolithic integration of flexible lithium-ion battery on a plastic substrate by printing methods

En ergy storage devices with flexible form factor have become critical components of wearable electr onic systems.In spired by methods of monolithic integration in the microelectronics fabrication process,we propose a planar flexible full-solid-state lithium-ion battery(FSLB)architecture and a layer-by-layer stencil printing assembly method for fabricating batteries on polyethylene terephthalate(PET)substrate.FSLBs use quasi-solid electrolyte based on LiTFSI and ultraviolet(UV)-curable ethoxylated trimethylolpropane triacrylate(ETPTA)polymeric matrix in combination with Li4Ti50i2(LTO)/LiFePO4(LFP)-based electrodes.Excellent mechanical flexibility(<10 mm bending radius)can be achieved.The electrochemical characteristics of electrolyte,including ion conductivity,physical stability during room-temperature and tender assembly processes,are promising.A complete thin film-shape FSLB demonstrated working operation both under planar and bending conditions.The unique architecture and assembly processes open new ways for planar flexible devices to be integrated with flexible energy devices.

Toward memristive in-memory computing:principles and applications

With the rapid growth of computer science and big data,the traditional von Neumann architecture suffers the aggravating data communication costs due to the separated structure of the processing units and memories.Memristive in-memory computing paradigm is considered as a prominent candidate to address these issues,and plentiful applications have been demonstrated and verified.These applications can be broadly categorized into two major types:soft computing that can tolerant uncertain and imprecise results,and hard computing that emphasizes explicit and precise numerical results for each task,leading to different requirements on the computational accuracies and the corresponding hardware solutions.In this review,we conduct a thorough survey of the recent advances of memristive in-memory computing applications,both on the soft computing type that focuses on artificial neural networks and other machine learning algorithms,and the hard computing type that includes scientific computing and digital image processing.At the end of the review,we discuss the remaining challenges and future opportunities of memristive in-memory computing in the incoming Artificial Intelligence of Things era.

Memristive structure of Nb/HfOx/Pd with controllable switching mechanisms to perform featured actions in neuromorphic networks

All memristor neuromorphic networks have great potential and advantage in both technology and computational protocols for artificial intelligence.It is crucial to find suitable elementary units for both performing featured neuromorphic functions and fabrication in large scale.Here a simple memristive structure,Nb/HfOx/Pd,is proposed for this goal.Its two resistive switching mechanisms,Mott transition of NbO2 and oxygen vacancy(Vo)migration,can be controlled by modulating external bias directions.Negative bias activates reversible phase transition and restrains Vo filament formation to allow the memristor to mimic the firing action potential.Positive bias activates Vo filament formation and restrains the other to allow the memristor to mimic synaptic plasticity and learning protocols.The system can respond adaptively to naturally generated action potentials and modified synaptic signals from the same memristive structure.In addition,some special features related to signal encoding and recognition are discovered when the system is settled according to chaos circuit theory.Our study provides a novel approach for designing elementary units for neuromorphic computations.

Application of mathematical morphology operation with memristor-based computation-in-memory architecture for detecting manufacturing defects

Mathematical morphology operations are widely used in image processing such as defect analysis in semiconductor manufacturing and medical image analysis.These data-intensive applications have high requirements during hardware implementation that are challenging for conventional hardware platforms such as central processing units(CPUs)and graphics processing units(GPUs).Computation-in-memory(CIM)provides a possible solution for highly efficient morphology operations.In this study,we demonstrate the application of morphology operation with a novel memristor-based auto-detection architecture and demonstrate non-neuromoq)hic computation on a multi-array-based memristor system.Pixel-by-pixel logic computations with low parallelism are converted to parallel operations using memristors.Moreover,hardware-implemented computer-integrated manufacturing was used to experimentally demonstrate typical defect detection tasks in integrated circuit(IC)manufacturing and medical image analysis.In addition,we developed a new implementation scheme employing a four-layer network to realize small-object detection with high parallelism.The system benchmark based on the hardware measurement results showed significant improvement in the energy efficiency by approximately 358 times and 32 times more than when a CPU and GPU were employed,respectively,exhibiting the advantage of the proposed memristor-based morphology operation.