Abstract: Continuous polysilicon on oxide diffusion edge (CPODE) processes are described herein in which one or more semiconductor device parameters are tuned to reduce the likelihood of etching of source/drain regions on opposing sides of CPODE structures formed in a semiconductor device, to reduce the likelihood of depth loading in the semiconductor device, and/or to reduce the likelihood of gate deformation in the semiconductor device, among other examples. Thus, the CPODE processes described herein may reduce the likelihood of epitaxial damage to the source/drain regions, may reduce current leakage between the source/drain regions, and/or may reduce the likelihood of threshold voltage shifting for transistors of the semiconductor device. The reduced likelihood of threshold voltage shifting may provide more uniform and/or faster switching speeds for the transistors, more uniform and/or lower power consumption for the transistors, and/or increased device performance for the transistors, among other examples.

Abstract: A scan flip-flop circuit includes first and second I/O nodes, a flip-flop circuit, a selection circuit configured to receive a scan direction signal and including input terminals coupled to the first and second I/O nodes and an output terminal coupled to an input terminal of the flip-flop circuit, and first and second drivers configured to receive the scan direction signal and a scan enable signal, each including an input terminal coupled to an output terminal of the flip-flop circuit and an output terminal coupled to a respective first or second input terminal of the selection circuit. Responsive to the scan direction and scan enable signals, one of the first driver is configured to output a first signal responsive to a second signal received at the second input terminal or the second driver is configured to output a third signal responsive to a fourth signal received at the first input terminal.

Abstract: A method for measuring a growth height of a plant, an electronic device, and a storage medium are provided. The method controls a camera device to obtain a color image and a depth image of a plant to be detected. The color image is detected by a detection model which is pre-trained, and a plurality of detection boxes which includes a plurality of plants to be detected is obtained. The color image and the depth image are aligned to create an alignment image. A plurality of target boxes is acquired from the alignment image, and depth values of the plurality of target boxes are determined. The quantity of the target boxes and a height of one or more plants to be detected are determined, no manual operations are required.

Abstract: A metal interconnect structure includes a first metal interconnection in an inter-metal dielectric (IMD) layer on a substrate, a second metal interconnection on the first metal interconnection, and a cap layer between the first metal interconnection and the second metal interconnection. Preferably, a top surface of the first metal interconnection is even with a top surface of the IMD layer and the cap layer is made of conductive material.

Abstract: A secondary-side control method of a flyback power converter includes a primary controller included in the flyback power converter generating a first gate control signal to turn on a power switch at a first predetermined valley of a first voltage after the primary controller enters a start-up mode; and a secondary controller included in the flyback power converter generating a trigger pulse to a synchronous switch at a second predetermined valley of a second voltage to make the primary controller enter a secondary-side control mode from the start-up mode after the secondary controller detects a first coupling voltage corresponding to the first gate control signal on the second voltage.

Abstract: A method for detecting image abnormities, applied in an electronic device, and stored in a storage medium are provided, obtains images for analysis. The images are divided into a plurality of first divided images and a plurality of second divided images by reference to image size. A first abnormity score is obtained by inputting the image into a first pre-trained abnormity detection model. A plurality of second abnormity scores are obtained by inputting the first divided images into a second pre-trained abnormity detection model. A plurality of third abnormity scores are obtained by inputting the second divided images into a third pre-trained abnormity detection model. An abnormal type of the image is determined according to a preset abnormity database in response to an abnormity detected in the image, the method improves accuracy of defect detection.

Abstract: A decision variable calculation method allowing a of reverse derivation requester to calculate decision variables based on a target result by pre-trained models provided by some participants in federated learning. The method includes the steps of providing the target result to each pre-trained model participating in this method and allowing them to reversely derive the input parameters, forming a loss function based on the difference between each pre-trained model and target result, integrating all input parameters into a total input parameter, and integrating all loss functions into a total loss function. An optimization problem is then constructed by the total input parameters and the total loss function. The solution to the optimization problem is the required decision variables.

Abstract: A method for calculating feasible process parameters of the present invention constructs an optimization model by treating a trained predictive model as a function of process parameters to be determined, wherein the objective function is to minimize the difference between the function value and the target result, subject to the conditions that the process parameters to be determined must satisfy, either individually or in relation to each other. Moreover, in order to solve the constrained optimization problem, the method with penalty function and barrier function can be used to convert the constrained optimization problem into an unconstrained optimization problem to make it more convenient to solve.

Abstract: A method for calculating decision variables is configured to calculate the unconfirmed decision variables. First, the method provides a trained predictive mode obtained by machine learning through a machine learning method on a dataset. Next, the method transforms the objective function of the trained predictive model from a constrained objective function to an unconstrained objective function. The method then solves the optimization problem of the unconstrained objective function, wherein the optimizer, trained with the trained predictive model, calculates gradients to facilitate the solution process. Additionally, the samples from the dataset used to train the trained predictive model can be utilized to determine the initial samples for solving the optimization problem. The method for calculating decision variables of the invention can also add a dummy layer in front of the trained predictive model.

Abstract: A method for calculating feasible process parameters to achieve a given process result and the method comprises the following steps of: providing a trained prediction model, obtained by machine learning of a dataset by a machine learning method, wherein the dataset comprises a plurality of samples, each of the samples comprises a plurality of sample parameters, and the trained predictive model is configured to input a plurality of input parameters and generate a prediction result corresponding to the input parameters; setting an expected result as the prediction result of the trained predictive model and providing at least one confirmed input parameters of the input parameters; and comparing the expected result, the at least one confirmed input parameters, and the sample parameters of the samples in the dataset by a reverse derivation algorithm to determine at least one non-confirmed input parameters of the input parameters.

Abstract: Provided are semiconductor devices and methods for fabricating such devices. An exemplary method includes forming a fin structure over a semiconductor material; forming a sacrificial layer over the semiconductor material; removing a portion of the fin structure and an overlying portion of the sacrificial layer located over the portion of the fin structure to form a trench; forming an insulation structure in the trench, wherein an adjacent portion of the sacrificial layer is adjacent an end wall of the insulation structure; removing the adjacent portion to form a cavity partially defined by the end wall; lining the cavity with a liner, wherein an end portion of the liner is located on the end wall of the insulation structure; filling the cavity with a fill material; removing the end portion of the liner to form an opening; and forming an end isolation structure in the opening.

Abstract: Methods for fabricating semiconductor devices are provided. An exemplary method includes forming fins in a dense region and in an isolated region of a semiconductor substrate; performing a plasma dry etch process to remove a portion of at least one selected fin to form a first trench in the dense region and to remove a portion of at least one selected fin in the isolated region to form a second trench in the isolated region, wherein the plasma dry etch process includes: performing a passivation-oriented process and an etchant-oriented process; and controlling the passivation-oriented process and the etchant-oriented process to form the first trench with a desired first critical dimension and first depth and to form the second trench with a desired second critical dimension and second depth.

Abstract: A federated learning contribution calculation method comprises the following steps: a plurality of participants collaboratively developing a federated aggregation model by federated learning method according to their own local datasets; excluding the participation of at least one first participant in all participants, and then the remained participants collaboratively developing a contribution model by federated learning method; and, comparing the value of the first contribution model and the value of the federated model to obtain the contribution of the at least one first participant. The method of the present invention is capable of calculating the contribution(s) of single participant or multiple participants in the federated learning by few additional information and few additional calculations, so as to achieve the fair profit sharing according to the contributions.

Abstract: The present invention provides a method for calculating decision variables. A dummy layer is added at an input layer of a trained neural network predictive model. The dummy layer includes a plurality of artificial neurons respectively connected to a corresponding input terminal of the input layer for the trained predictive model by a newly established link. The input value of each artificial neuron is set to 1, the bias value of the activation function is set to 0, and the output of the activation function is set to 1 when the input of the activation function is 1. The initial weight value of the newly established link is selected and set, and the weight values can be considered as decision variables, wherein the weight values can have ranges or other inter-conditional restrictions.

Abstract: A method for determining a growth height of a plant, an electronic device, and storage medium are provided. The method includes controlling a camera device to capture a plant to be detected, and obtaining a color image and a depth image of the plant to be detected. The color image and the depth image are aligned and an alignment image is obtained. The color image is detected using a pre-trained mobilenet-ssd network, and a detection box including the plant to be detected is obtained. A depth value of each of pixel points in the detection box is determined, and target depth values are obtained. A mean value and a standard deviation of the target depth values are determined, and a height of the plant to be detected is determined. According to the method, accuracy of the height of the plant can be improved.

Abstract: A method for detecting medical images implemented in an electronic device includes obtaining at least one image to be detected; obtaining a reconstructed image by inputting the at least one image to be detected as a target image into a pre-trained variational autoencoder model; determining a target area according to pixel values of pixels in the reconstructed image and the target image; obtaining a feature area and a lesion category of the feature area by inputting the target image into a pre-trained convolutional neural network model; when there is a feature area corresponding to the target area in the target image, determining a lesion area and a corresponding lesion category based on the target area and the feature area, and generating a detection result of the image to be detected.

Abstract: A method for evaluating environment and surroundings of a pedestrian passageway, used in an electronic device, obtains a position information of a target area, and obtains a streetscape image corresponding to the position information of the area. The method further inputs the streetscape image into a trained convolutional neural network, makes the trained convolutional neural network carry out a convolution calculation of the streetscape image to generate a feature vector for classifying a number of target objects in the streetscape image, and outputs the feature vector. The feature vector is input into a full convolution neural network to apply a certain color to a number of pixels belonging to a same target object, and outputs the streetscape image with colored target objects.

Abstract: A method of detecting defects revealed in images of finished products includes importing flawless images into an autoencoder for model training and obtaining reconstructed images from them. The reconstructed images are compared with the flawless images to obtain groups of test errors. An error threshold is selected from the groups of the test errors according to preset rules. In practice, obtaining an image to be tested, and a reconstructed image to be tested, and an error to be tested; determining detection of the image to be tested according to the error and the error threshold; and importing the image into a classifier for defect classification if the image is found to reveal a defect. An electronic device and a non-volatile storage medium for performing the above-described method, are also disclosed.

Abstract: A method of detecting product defects obtains an image of a product and sets a region of interest (ROI) of the image. A first contour of a first target object is detected in the region of interest. The image is detected according to the first contour to obtain a corrected image. A position difference between the first contour and a second target object in the region of interest is obtained. A second contour of the second target object is detected in the corrected image according to the position difference. A first image area corresponding to the first contour and a second image area corresponding to the second contour are segmented and input into an autoencoder. According to outputs of the autoencoder, whether the product is defective is determined. A detection result of the product is output. The method can detect defects on products quickly and accurately.

Abstract: A method applied in an electronic device for detecting and classifying apparent defects in images of products inputs an image to a trained autoencoder to obtain a reconstructed image, determines whether the image reveals defects based on a defect criterion for filtering out small noise reconstruction errors. If so revealed, the electronic device calculates a plurality of structural similarity values between the image and a plurality of template images with marked defect categories, determines a target defect category corresponding to the highest structural similarity value, and classifies the defect revealed in the image into the target defect category.