Abstract: A structure and a method of forming are provided. The structure includes a first dielectric layer overlying a first substrate. A first connection pad is disposed in a top surface of the first dielectric layer and contacts a first redistribution line. A first dummy pad is disposed in the top surface of the first dielectric layer, the first dummy pad contacting the first redistribution line. A second dielectric layer overlies a second substrate. A second connection pad and a second dummy pad are disposed in the top surface of the second dielectric layer, the second connection pad bonded to the first connection pad, and the first dummy pad positioned in a manner that is offset from the second dummy pad so that the first dummy pad and the second dummy pad do not contact each other.

Abstract: Provided is an electronic package, in which a heat dissipating body is formed on an electronic device and is combined with a heat sink so that the electronic device, the heat dissipating body and the heat sink form a receiving space, and a heat dissipating material is formed in the receiving space and in contact with the heat sink and the electronic device, where a fluid regulating space is formed between the heat dissipating material and the heat dissipating body and is used as a volume regulating space for the heat dissipating material during thermal expansion and contraction.

Abstract: A first and second semiconductor device are bonded together using a bonding contact pad embedded within a bonding dielectric layer of the first semiconductor device and at least one bonding via embedded within a bonding dielectric layer of the second semiconductor device. The bonding contact pad extends a first dimension in a first direction perpendicular to the major surface of the first semiconductor device and a second dimension in a second direction parallel to the plane of the first semiconductor wafer, the second dimension being at least twice the first dimension. The bonding via extends a third dimension in the first direction and a fourth dimension in the second direction, the third dimension being at least twice the first dimension. The bonding contact pad and bonding via may be at least partially embedded in respective bonding dielectric layers in respective topmost dielectric layers of respective stacked interconnect layers.

Abstract: In a method of manufacturing a semiconductor device, a fin structure is formed by patterning a semiconductor layer, and an annealing operation is performed on the fin structure. In the patterning of the semiconductor layer, a damaged area is formed on a sidewall of the fin structure, and the annealing operation eliminates the damaged area.

Abstract: A method of forming a semiconductor structure is provided, and includes trimming a first substrate to form a recess on a sidewall of the first substrate. A conductive structure is formed in the first substrate. The method includes bonding the first substrate to a carrier. The method includes thinning down the first substrate. The method also includes forming a dielectric material in the recess and over a top surface of the thinned first substrate. The method further includes performing a planarization process to remove the dielectric material and expose the conductive structure over the top surface. In addition, the method includes removing the carrier from the first substrate.

Abstract: A method of forming an integrated circuit, including forming a n-type doped well (N-well) and a p-type doped well (P-well) disposed side by side on a semiconductor substrate, forming a first fin active region extruded from the N-well and a second fin active region extruded from the P-well, forming a first isolation feature inserted between and vertically extending through the N-well and the P-well, and forming a second isolation feature over the N-well and the P-well and laterally contacting the first and the second fin active regions.

Abstract: In some embodiments, the present disclosure relates to a wafer edge trimming apparatus that includes a processing chamber defined by chamber housing. Within the processing chamber is a wafer chuck configured to hold onto a wafer structure. Further, a blade is arranged near an edge of the wafer chuck and configured to remove an edge potion of the wafer structure and to define a new sidewall of the wafer structure. A laser sensor apparatus is configured to direct a laser beam directed toward a top surface of the wafer chuck. The laser sensor apparatus is configured to measure a parameter of an analysis area of the wafer structure. Control circuitry is to the laser sensor apparatus and the blade. The control circuitry is configured to start a damage prevention process when the parameter deviates from a predetermined threshold value by at least a predetermined shift value.

Abstract: A fin field effect transistor (FinFET) device structure is provided. The FinFET device structure includes a plurality of fin structures above a substrate, an isolation structure over the substrate and between the fin structures, and a gate structure formed over the fin structure. The FinFET device structure includes a source/drain (S/D) structure over the fin structure, and the S/D structure is adjacent to the gate structure. The FinFET device structure also includes a metal silicide layer over the S/D structure, and the metal silicide layer is in contact with the isolation structure.

Abstract: A high-voltage semiconductor device structure is provided. The high-voltage semiconductor device structure includes a semiconductor substrate, a source ring in the semiconductor substrate, and a drain region in the semiconductor substrate. The high-voltage semiconductor device structure also includes a doped ring surrounding sides and a bottom of the source ring and a well region surrounding sides and bottoms of the drain region and the doped ring. The well region has a conductivity type opposite to that of the doped ring. The high-voltage semiconductor device structure further includes a conductor electrically connected to the drain region and extending over and across a periphery of the well region. In addition, the high-voltage semiconductor device structure includes a shielding element ring between the conductor and the semiconductor substrate. The shielding element ring extends over and across the periphery of the well region.

Abstract: A method includes forming a first sealing layer at a first edge region of a first wafer; and bonding the first wafer to a second wafer to form a wafer stack. At a time after the bonding, the first sealing layer is between the first edge region of the first wafer and a second edge region of the second wafer, with the first edge region and the second edge region comprising bevels. An edge trimming process is then performed on the wafer stack. After the edge trimming process, the second edge region of the second wafer is at least partially removed, and a portion of the first sealing layer is left as a part of the wafer stack. An interconnect structure is formed as a part of the second wafer. The interconnect structure includes redistribution lines electrically connected to integrated circuit devices in the second wafer.

Abstract: A method includes forming a transistor, which includes forming a dummy gate stack over a semiconductor region, and forming an Inter-Layer Dielectric (ILD). The dummy gate stack is in the ILD, and the ILD covers a source/drain region in the semiconductor region. The method further includes removing the dummy gate stack to form a trench in the first ILD, forming a low-k gate spacer in the trench, forming a replacement gate dielectric extending into the trench, forming a metal layer to fill the trench, and performing a planarization to remove excess portions of the replacement gate dielectric and the metal layer to form a gate dielectric and a metal gate, respectively. A source region and a drain region are then formed on opposite sides of the metal gate.

Abstract: Method and system for classifying and labeling images, which can perform segmentation based on features of each part of images, classify and match the image and the segmented image based on a classification model built by the machine learning method. Meanwhile, each image is assigned with labels and text descriptions. The system also includes a string module assigning the image with a plurality of matching labels and text descriptions that are the most relevant in recent times. Furthermore, the classification model is trained by machine learning method such as an unsupervised learning, a self-supervised learning, or a heuristic algorithms. In addition, a character recognition module is provided to extract characters in the image for comprehensive learning and calculations to facilitate classification and labeling of the image.

Abstract: Method and system for extracting valuable words and forming a valuable word net, wherein a server is employed to collect contents of articles from internet sources, EDM texts (email direct marketing), product descriptions and other texts, and extract valuable words by machine learning. Each valuable word performs connection weight based on the number of times the text has been read, the number of times the text has been clicked, the number of times the text has been cited, the correlation of external sites, the conversion of expert knowledge, the probability space, the Shannon entropy, the spatial distribution and other values and their conversion by machine learning; then the linked valuable words are integrated to form a valuable word net. When the valuable words are needed to be used, the valuable words and the valuable word net can be retrieved from the database for subsequent applications.

Abstract: An electronic marketing system and an electronic marketing method, wherein, by retrieving the user's browsing traces, browsing history and other de-identified information on the website or the Internet, the similar users can be still effectively grouped without using the user's personal information. The product can be matched with the user to filter out the candidate products that the user may purchase. Alternatively, the product to be sold can be selected first to conduct the matching process, thereby creating a candidate user group. Thereafter, the product leaflet can be generated from candidate products and sent to each user in the candidate user group. Moreover, the user information can be adjusted in real time after the user clicks on the product leaflet. The targeted marketing can still be achieved without use of personal information. Furthermore, the user information can be adjusted in real time to achieve the optimal electronic marketing effect.

Abstract: A system for awakening non-shopping consumers and an implementation method thereof employ artificial intelligence to grouping consumers, and then determine consumers who have not shopped for a long time, and provide products that meet consumers' needs by analyzing the results of the grouping, as the basis for consumers to shop. Moreover, the desire of consumers to buy goods can be promoted so as to awaken consumers who have not shopped for a long time.

Abstract: A method for behavior vectorization of information de-identification, through which data concerning browsing traces, link paths, trigger events, clicks, and operation behaviors of network users on the Internet are selected by a server, a client device, or an edge device for performing a conversion/integration process. Then, the integrated data are converted into a vector. The vector represents the profile of the usage behavior of the network users. Moreover, because vectors can be quickly grouped and classified to find similar groups, it can quickly identify the network users. The server uses the supervised learning method as the base method, and uses pre-defined network behaviors for training. Also, the semi-supervised learning method or the unsupervised learning method can be employed to modify undefined network behaviors to better conform to the profile description of the network users.

Abstract: Method and system for classifying and labeling images, which can perform segmentation based on features of each part of images, classify and match the image and the segmented image based on a classification model built by the machine learning method. Meanwhile, each image is assigned with labels and text descriptions. The system also includes a string module assigning the image with a plurality of matching labels and text descriptions that are the most relevant in recent times. Furthermore, the classification model is trained by machine learning method such as an unsupervised learning, a self-supervised learning, or a heuristic algorithms. In addition, a character recognition module is provided to extract characters in the image for comprehensive learning and calculations to facilitate classification and labeling of the image.

Abstract: Method and system for extracting valuable words and forming a valuable word net, wherein a server is employed to collect contents of articles from internet sources, EDM texts (email direct marketing), product descriptions and other texts, and extract valuable words by machine learning. Each valuable word performs connection weight based on the number of times the text has been read, the number of times the text has been clicked, the number of times the text has been cited, the correlation of external sites, the conversion of expert knowledge, the probability space, the Shannon entropy, the spatial distribution and other values and their conversion by machine learning; then the linked valuable words are integrated to form a valuable word net. When the valuable words are needed to be used, the valuable words and the valuable word net can be retrieved from the database for subsequent applications.

Abstract: Method and system for determining and reclassifying valuable words, wherein a large amount of text and valuable words are pre-inputted into a word processing server for machine learning. Moreover, the word processing server is trained on the valuable words and many labels associated with the valuable words such that it can learn and determines the valuable words in the text that meet the definition of the valuable word. The valuable word is further extracted from the text and re-classified after extraction. In addition, each valuable word is provided with various relevance labels to facilitate the subsequent application of the valuable words.