Abstract: Disclosed is an electronic device including a device body and an antenna module. The antenna module includes a conductive element and at least one antenna element. The conductive element includes a main body portion and at least one assembly portion connected with each other. The at least one assembly portion is assembled on the device body. The at least one antenna element is disposed on the device body and coupled with the conductive element to excite a first resonance mode. The at least one assembly portion overlaps the at least one antenna element in the length direction of the main body portion.

Abstract: The present disclosure provides a semiconductor device. The semiconductor device includes a fin disposed in a first region of the semiconductor device, channel members disposed in a second region of the semiconductor device and stacked in a vertical direction, first and second metal gates disposed on a top surface of the fin, a third metal gate wrapping around each of the channel members, a first implant region in the fin with a first conductivity type, and a second implant region in the fin with a second conductivity opposite the first conductivity type. The fin includes first and second type epitaxial layers alternatingly disposed in the vertical direction. The first and second type epitaxial layers have different material compositions. The first type epitaxial layers and the channel members have the same material composition.

Abstract: A high electron mobility transistor device including a channel layer, a first barrier layer, and a P-type gallium nitride layer is provided. The first barrier layer is disposed on the channel layer. The P-type gallium nitride layer is disposed on the first barrier layer. The first thickness of the first barrier layer located directly under the P-type gallium nitride layer is greater than the second thickness of the first barrier layer located on two sides of the P-type gallium nitride layer.

Abstract: In general, the disclosure describes techniques for Artificial Intelligence (AI) models that can automatically generate diverse, explainable, interpretable, reactive, and coordinated behaviors for a team. In an example, a method includes receiving multimodal input data within a simulator configured to simulate solving a predefined problem by a team including a plurality of agents; generating one or more generative neural network models based on the multimodal input data and based on a predetermined threshold of success of problem solving in the simulator; outputting, by the one or more generative neural network models, one or more multi-agent controllers, wherein each of the one or more multi-agent controllers comprises recommended behaviors for each of the plurality of agents to solve the predefined problem in a manner that is consistent with the multimodal input data.

Abstract: In general, the disclosure describes techniques for parameterizing building information using images. In an example, a method includes receiving an image of a building; applying, by a machine learning system, a machine learning model to the received image of a building to generate, for a new building, new building parameters to be input to a building information modeling (BIM) data generation system, wherein the machine learning model is trained using images of buildings and corresponding building parameters for the buildings; and outputting, by the machine learning system, the new building parameters for the new building.

Abstract: A method of forming a process film includes the following operations. A substrate is transferred into a process chamber having an interior surface. A process film is formed over the substrate, and the process film is also formed on the interior surface of the process chamber. The substrate is transferred out of the process chamber. A non-process film is formed on the interior surface of the process chamber. In some embodiments, porosity of the process film is greater than a porosity of the non-process film.

Abstract: A chemical vapor deposition (CVD) apparatus is provided. The CVD apparatus includes a CVD chamber including multiple wall portions. A pedestal is disposed inside the CVD chamber, configured to support a substrate. A gas inlet port is disposed on one of the wall portions and below a substrate support portion of the pedestal. In addition, a gas flow guiding member is disposed inside the CVD chamber, coupled to the gas inlet port, and configured to dispense cleaning gases from the gas inlet port into the CVD chamber.

Abstract: In general, the disclosure describes various aspects of techniques for evaluating decisions determined by autonomous devices. A device comprising a memory and a processor may be configured to perform the techniques. The memory may store first state data representative of a first observational state detected by an autonomous device, and first action data representative of one or more first actions the autonomous device performs responsive to detecting the first observational state. The processor may execute a computation engine configured to identify, based on the first action data, a first inflection point representative of changing behavior of the autonomous device. The computation engine may further be configured to determine, based on the first inflection point, first explanatory data representative of portions of the first state data on which the autonomous device relied that explain the changing behavior of the autonomous device, and output the first explanatory data.

Abstract: Techniques are disclosed for training a machine learning model to perform actions within an environment. In one example, an input device receives a declarative statement. A computation engine selects, based on the declarative statement, a template that includes a template action performable within the environment. The computation engine generates, based on the template, synthetic training episodes. The computation engine further generates experiential training episodes, each experiential training episode collected by a machine learning model from past actions performed by the machine learning model. Each synthetic training episode and experiential training episode comprises an action and a reward. A machine learning system trains, with the synthetic training episodes and the experiential training episodes, the machine learning model to perform the actions within the environment.

Abstract: A chemical vapor deposition (CVD) apparatus is provided. The CVD apparatus includes a CVD chamber including multiple wall portions. A pedestal is disposed inside the CVD chamber, configured to support a substrate. A gas inlet port is disposed on one of the wall portions and below a substrate support portion of the pedestal. In addition, a gas flow guiding member is disposed inside the CVD chamber, coupled to the gas inlet port, and configured to dispense cleaning gases from the gas inlet port into the CVD chamber.

Abstract: Techniques are disclosed for using a computation engine executing a machine learning system to generate, according to constraints, renderings of a building or building information modeling (BIM) data for the building, wherein the constraints include at least one of an architectural style or a building constraint. In one example, an input device is configured to receive an input indicating one or more surfaces for the building and one or more constraints. A machine learning system executed by a computation engine is configured to apply a model, trained using images of buildings labeled with corresponding constraints for the buildings, to the one or more surfaces for the building to generate at least one of a rendering of the one or more surfaces for the building according to the constraints or BIM data for the building according to the constraints. Further, the machine learning system is configured to output the rendering or the BIM data.

Abstract: In general, the disclosure describes techniques for machine learning for translation to structured computer readable representation. An example method to generate a training set for a natural language translation model includes receiving, by a computing system, a grammar comprising rules, one or more of the rules being associated with random biases; generating, by the computing system, at least one of random trees or random graphs based on the random biases in the grammar; for each of the random trees or random graphs, by the computing system, generating a natural language sample; and generating, by the computing system, the training set with the random trees or random graphs and the corresponding natural language samples.

Abstract: Disclosed techniques can generate content object summaries. Content of a content object can be parsed into a set of word groups. For each word group, at least one topic to which the word group pertains can be identified and it can be determined, via a user model, at least one weight of the plurality of weights corresponding to the topic(s). For each word group, a score can be determined for the word group based on the weight(s). A subset of the set of word groups can be selected based on the scores for the word group. A summary of the content object can be generated that includes the subset but that does not include one or more other word groups in the set of word groups that are not in the subset. At least part of the summary of the content object can be output.

Abstract: Techniques are disclosed for training a machine learning model to perform actions within an environment. In one example, an input device receives a declarative statement. A computation engine selects, based on the declarative statement, a template that includes a template action performable within the environment. The computation engine generates, based on the template, synthetic training episodes. The computation engine further generates experiential training episodes, each experiential training episode collected by a machine learning model from past actions performed by the machine learning model. Each synthetic training episode and experiential training episode comprises an action and a reward. A machine learning system trains, with the synthetic training episodes and the experiential training episodes, the machine learning model to perform the actions within the environment.

Abstract: Techniques are disclosed for applying a multi-level introspection framework to interaction data characterizing a history of interaction of a reinforcement learning agent with an environment. The framework may apply statistical analysis and machine learning methods to interaction data collected during the RL agent's interaction with the environment. The framework may include a first (“environment”) level that analyzes characteristics of one or more tasks to be solved by the RL agent to generate elements, a second (“interaction”) level that analyzes actions of the RL agent when interacting with the environment to generate elements, and a third (“meta-analysis”) level that generates elements by analyzing combinations of elements generated by the first level and elements generated by the second level.

Abstract: A method of forming a process film includes the following operations. A substrate is transferred into a process chamber having an interior surface. A process film is formed over the substrate, and the process film is also formed on the interior surface of the process chamber. The substrate is transferred out of the process chamber. A non-process film is formed on the interior surface of the process chamber. In some embodiments, porosity of the process film is greater than a porosity of the non-process film.

Abstract: In general, the disclosure describes various aspects of techniques for evaluating decisions determined by autonomous devices. A device comprising a memory and a processor may be configured to perform the techniques. The memory may store first state data representative of a first observational state detected by an autonomous device, and first action data representative of one or more first actions the autonomous device performs responsive to detecting the first observational state. The processor may execute a computation engine configured to identify, based on the first action data, a first inflection point representative of changing behavior of the autonomous device. The computation engine may further be configured to determine, based on the first inflection point, first explanatory data representative of portions of the first state data on which the autonomous device relied that explain the changing behavior of the autonomous device, and output the first explanatory data.

Abstract: A method of cleaning a process chamber includes following steps. A plurality of process films and a plurality of non-process films are alternately formed on an interior surface of the process chamber. A cleaning operation is performed to remove the plurality of process films and the plurality of non-process films from the interior surface of the process chamber.

Abstract: Techniques are disclosed for using a computation engine executing a machine learning system to generate, according to constraints, renderings of a building or building information modeling (BIM) data for the building, wherein the constraints include at least one of an architectural style or a building constraint. In one example, an input device is configured to receive an input indicating one or more surfaces for the building and one or more constraints. A machine learning system executed by a computation engine is configured to apply a model, trained using images of buildings labeled with corresponding constraints for the buildings, to the one or more surfaces for the building to generate at least one of a rendering of the one or more surfaces for the building according to the constraints or BIM data for the building according to the constraints. Further, the machine learning system is configured to output the rendering or the BIM data.

Abstract: A method of cleaning a process chamber includes following steps. A plurality of process films and a plurality of non-process films are alternately formed on an interior surface of the process chamber. A cleaning operation is performed to remove the plurality of process films and the plurality of non-process films from the interior surface of the process chamber.