Abstract: A semiconductor light-emitting device comprises a substrate; a first adhesive layer on the substrate; multiple epitaxial units on the first adhesive layer; a second adhesive layer on the multiple epitaxial units; multiple first electrodes between the first adhesive layer and the multiple epitaxial units, and contacting the first adhesive layer and the multiple epitaxial units; and multiple second electrodes between the second adhesive layer and the multiple epitaxial units, and contacting the second adhesive layer and the multiple epitaxial units; wherein the multiple epitaxial units are totally separated.

Abstract: An execution method of an online discussion board and a server using the same are provided. The server includes an information collection unit, an analyzing unit, a discussion board management unit and a recording unit. The information collection unit is configured to obtain a plurality of process problem messages. The analyzing unit is configured to automatically obtain a process analysis report according to the process problem messages. The discussion board management unit is configured to automatically create the online discussion board. At least two users are invited to enter the online discussion board. The process analysis report is shown in the online discussion board. The recording unit is configured to store a discussion record on the online discussion board. The online discussion board is held for a period of time.

Abstract: A semiconductor light-emitting device comprises a substrate; a first adhesive layer on the substrate; multiple epitaxial units on the first adhesive layer; a second adhesive layer on the multiple epitaxial units; multiple first electrodes between the first adhesive layer and the multiple epitaxial units, and contacting the first adhesive layer and the multiple epitaxial units; and multiple second electrodes between the second adhesive layer and the multiple epitaxial units, and contacting the second adhesive layer and the multiple epitaxial units; wherein the multiple epitaxial units are totally separated.

Abstract: A semiconductor manufacturing process prediction method and a semiconductor manufacturing process prediction device are provided. The semiconductor manufacturing process prediction method includes the following steps. A plurality of process data are obtained. According to the process data, a machine learning model is used to execute prediction and obtain a prediction confidence and a prediction yield. Whether the prediction confidence is lower than a predetermined level is determined. If the prediction confidence is lower than the predetermined level, the machine learning model is modified. According to the process data, the prediction yield is adjusted.

Abstract: A training method of a semiconductor process prediction model, a semiconductor process prediction device, and a semiconductor process prediction method are provided. The training method of the semiconductor process prediction model includes the following steps. The semiconductor process was performed on several samples. A plurality of process data of the samples are obtained. A plurality of electrical measurement data of the samples are obtained. Some of the samples having physical defects are filtered out according to the process data. The semiconductor process prediction model is trained according to the process data and the electrical measurement data of the filtered samples.

Abstract: An operation method and an operation device of a failure detection and classification (FDC) model are provided. The operation method of the FDC model includes the following steps. A plurality of raw traces are continuously obtained. If the raw traces have started to be changed from the first waveform to the second waveform, whether at least N pieces in the race traces have been changed to the second waveform is determined. If at least N pieces in the raw traces have been changed to the second waveform, the raw traces which have been changed to the second waveform are automatically segmented to obtain several windows. An algorithm is automatically set for each of the windows. Through each of the algorithms, an indicator of each of the windows is obtained. The FDC model is retrained based on these indicators.

Abstract: Disclosed is a light-emitting device comprising a light-emitting stack having a length, a width, a first semiconductor layer, an active layer on the first semiconductor layer, and a second semiconductor layer on the active layer, wherein the first semiconductor layer, the active layer, and the second semiconductor layer are stacked in a stacking direction. A first electrode is coupled to the first semiconductor layer and extended in a direction parallel to the stacking direction and a second electrode is coupled to the second semiconductor layer and extended in a direction parallel to the stacking direction. A dielectric layer is disposed between the first electrode and the second electrode.

Abstract: A manufacturing data analyzing method and a manufacturing data analyzing device are provided. The manufacturing data analyzing method includes the following steps. Each of at least one numerical data, at least one image data and at least one text data is transformed into a vector. The vectors are gathered to obtain a combined vector. The combined vector is inputted into an inference model to obtain a defect cause and a modify suggestion.

Abstract: An automatic adjusting method for equipment and a smart adjusting device using the same are provided. The automatic adjusting method of the equipment includes the following steps. A template frame from the equipment is obtained in an initial period. Several clear frames are obtained in one window period. Each of the template frame and the clear frame has a pixel variation. The pixel variation of the template frame is the largest in the initial period. The pixel variation of each of the clear frame is greater than a threshold. Each of the clear frame is compared with the template frame to obtain an offset. A statistical value of the offsets is calculated. A parameter of the equipment is adjusted to reduce the statistical value.

Abstract: An operation method and an operation device of a failure detection and classification (FDC) model are provided. The operation method of the FDC model includes the following steps. A plurality of raw traces are continuously obtained. If the raw traces have started to be changed from the first waveform to the second waveform, whether at least N pieces in the race traces have been changed to the second waveform is determined. If at least N pieces in the raw traces have been changed to the second waveform, the raw traces which have been changed to the second waveform are automatically segmented to obtain several windows. An algorithm is automatically set for each of the windows. Through each of the algorithms, an indicator of each of the windows is obtained. The FDC model is retrained based on these indicators.

Abstract: A semiconductor light-emitting device comprises a substrate; a first adhesive layer on the substrate; multiple epitaxial units on the first adhesive layer; a second adhesive layer on the multiple epitaxial units; multiple first electrodes between the first adhesive layer and the multiple epitaxial units, and contacting the first adhesive layer and the multiple epitaxial units; and multiple second electrodes between the second adhesive layer and the multiple epitaxial units, and contacting the second adhesive layer and the multiple epitaxial units; wherein the multiple epitaxial units are totally separated.

Abstract: A semiconductor light-emitting device comprises a substrate; a first adhesive layer on the substrate; multiple epitaxial units on the first adhesive layer; a second adhesive layer on the multiple epitaxial units; multiple first electrodes between the first adhesive layer and the multiple epitaxial units, and contacting the first adhesive layer and the multiple epitaxial units; and multiple second electrodes between the second adhesive layer and the multiple epitaxial units, and contacting the second adhesive layer and the multiple epitaxial units; wherein the multiple epitaxial units are totally separated.

Abstract: An automatic detecting device and an automatic detecting method of a manufacturing equipment are provided. The automatic detecting method of the manufacturing equipment includes the following steps. A detection curve of the manufacturing equipment executing several recipe steps is obtained. The detection curve is aligned to the recipe steps, such that the detection curve is divided into several process segments. At least one peak or at least one valley in each of the process segments is searched to obtain several sub-step segments. According to the sub-step segments, a Fault Detection Classification analysis (FDC) is performed to obtain an analysis result. Based on the analysis result, a predict health information of the manufacturing equipment is outputted.

Abstract: Embodiments of the invention include method, systems and computer program products for servicing indirect storage requests. Method includes decoding a storage request instruction and sending to a first one of a plurality of memory controllers an address represented by a first pointer associated with at least a portion of the storage request instruction. A first memory is used to read information associated with a second pointer contained at the address. The first memory forwards the storage request instruction to a second one of the plurality of memory controllers, wherein the second one of the plurality of memory controllers is associated with and/or manages a memory location represented by the second pointer. The second one of the plurality of memory controllers reads and forwards data associated with the storage request instruction to a processor using the second pointer. The processor writes the forwarded data in a destination register of the processor.

Abstract: An automatic adjusting method for equipment and a smart adjusting device using the same are provided. The automatic adjusting method of the equipment includes the following steps. A template frame from the equipment is obtained in an initial period. Several clear frames are obtained in one window period. Each of the template frame and the clear frame has a pixel variation. The pixel variation of the template frame is the largest in the initial period. The pixel variation of each of the clear frame is greater than a threshold. Each of the clear frame is compared with the template frame to obtain an offset. A statistical value of the offsets is calculated. A parameter of the equipment is adjusted to reduce the statistical value.

Abstract: A remote control device for a manufacturing equipment and a method for detecting manual control are provided. The method for detecting the manual control on the manufacturing equipment includes the following steps. A cursor pattern is created. When the user interface is automatically controlled, a history location of the cursor pattern shown on a user interface of the manufacturing equipment is detected to obtain a location distribution. The location distribution is stored. A current location of the cursor pattern shown on the user interface is detected. If the current location is not within the location distribution, it is deemed that the user interface is manually controlled.

Abstract: Embodiments include method, systems and computer program products for fusing one or more transaction request messages. The computer-implemented method includes comparing, using a memory controller, at least two electronic transaction request messages and determining if the at least two electronic transaction request messages are of a same electronic transaction request message type. The memory controller is used to determine that the at least two electronic transaction request messages are directed to associated portions of memory based at least in part on determining that the at least two electronic transaction request messages are the same electronic transaction request message type. The memory controller fuses the at least two electronic transaction request messages based at least in part on determining that the at least two electronic transaction request messages are directed to associated portions of memory.

Abstract: A semiconductor light-emitting device comprises a substrate; a first adhesive layer on the substrate; multiple epitaxial units on the first adhesive layer; a second adhesive layer on the multiple epitaxial units; multiple first electrodes between the first adhesive layer and the multiple epitaxial units, and contacting the first adhesive layer and the multiple epitaxial units; and multiple second electrodes between the second adhesive layer and the multiple epitaxial units, and contacting the second adhesive layer and the multiple epitaxial units; wherein the multiple epitaxial units are totally separated.

Abstract: A light-emitting device comprises a light-emitting stack; a reflective structure comprising a reflective layer on the light-emitting stack and a first insulating layer covering the reflective layer; and a first conductive layer on the reflective structure; wherein the first insulating layer isolates the reflective layer from the first conductive layer.

Abstract: A method for automatic inline detection and wafer disposition includes the following steps. An exposure process is performed to wafers in an exposure apparatus. A virtual inspection is performed based on log files of the exposure process. A wafer automatic disposition is performed according to a result of the virtual inspection. An automatic inline detection and wafer disposition system includes a first computer system coupled to an exposure apparatus and a second computer system coupled to the first computer system. The exposure apparatus is configured to perform an exposure process to wafers, and the first computer system is configured to perform a virtual inspection based on log files of the exposure process. The second computer system is configured to receive a result of the virtual inspection and perform a wafer automatic disposition according to the result of the virtual inspection.