Abstract: A method, apparatus, and system for controlling a multi-chamber system configured to process substrates are described herein. In some embodiments, a method comprises automatically determining, by each digital twin device a first data set associated with a corresponding process chamber of process chambers. The plurality of digital twin devices captures and models characteristics and processes of process chambers and generates control inputs for controlling the process chambers or processes executed by the chambers during the manufacturing of substrates. The method further comprises automatically generating, by each digital twin device, a second data set that comprises control inputs, and automatically transmitting the second data set to the process chamber.

Abstract: A method, apparatus, and system for controlling a physical twin chamber configured to process substrates are described herein. In some embodiments, a method comprises determining, by a digital twin device, characteristics of a physical twin chamber and generating control inputs for controlling the physical twin chamber. The digital twin device comprises one or more computational models for determining the characteristics of the physical twin and for generating the control inputs. The digital twin device determines a first data set associated with the physical twin chamber. The first data set comprises process data collected by sensors configured to measure attributes of the physical twin chamber. Based on the first data, the digital twin device automatically generates a second data set based on the generated control inputs and transmits the second data set to the physical twin chamber for controlling the process performed on the substrates by the physical twin chamber.

Abstract: A method, apparatus, and system for controlling a multi-chamber process system for substrate processing are described herein. In some embodiments, a method comprises determining, by each digital twin device, of a plurality of digital twin devices, a first data set associated with at least one process chamber of a plurality of chamber processes, and the corresponding processes for processing a plurality of substrates. Each digital twin device comprises one or more computational models. The first data set comprises measurements reported by probes or sensors within the at least one chamber process, or data collected and reported by internal sensors of the digital twin device. The method further automatically generating, by each digital twin device a second data set based on, at least in part, the first data set, and by executing one or more computational models of the digital twin device.

Abstract: The present disclosure discloses a question analysis method, a device, a knowledge base question answering system and an electronic equipment. The method includes: analyzing a question to obtain N linearized sequences, N being an integer greater than 1; converting the N linearized sequences into N network topology maps; separately calculating a semantic matching degree of each of the N network topology maps to the question; and selecting a network topology map having a highest semantic matching degree to the question as a query graph of the question from the N network topology maps. According to the technology of the present disclosure, the query graph of the question can be obtained more accurately, and the accuracy of the question to the query graph is improved, thereby improving the accuracy of question analysis.

Abstract: There is provided a method that is performed while a host and a guest are using a conversational messaging interface. For a message thread, in response to receiving a guest message, the method computes a probability for guest issues by inputting feature vectors from the guest message to a guest issue detection model. In response to receiving a host message, the message computes a probability for host issues by inputting feature vectors from the host message to a host issue detection model. If the message thread includes particular keywords, and if the probability for the guest issues is above a guest issue predetermined threshold, the method includes providing alerts that is visible only to the guest. If the probability for the host issues is above a host issue predetermined threshold, the method also includes providing alerts that is visible only to the host.

Abstract: A probe card, a method for designing the probe card, a method for producing a tested semiconductor device, a method for testing an unpackaged semiconductor by the probe card, a device under test, and a probe system are provided. The probe card includes a wiring substrate, a connection carrier board, and a probe device. At least two probes form a differential pair electrically connected to a loopback line of the connection carrier board to form a test signal loopback path. The probe device has a probe device impedance on the test signal loopback path. The loopback line has a loopback line impedance on the test signal loopback path. A difference between the probe device impedance on the test signal loopback path and the loopback line impedance on the test signal loopback path is in an impedance range.

Abstract: An evaluation method of mixing uniformity of composite powder includes: determining raw materials of composite powder to be evaluated and mass ratio; mixing to obtain multiple standard composite powder with different mixing time; determining flow energy of each standard composite powder; analyzing the flow energy of multiple standard composite powders by significant difference method, determining at least 3 consecutive standard composite powders with no significant difference in flow energy according to mixing time from small to large, defining as uniform-mixed standard composite powder, calculating average value of flow energy of uniform-mixed standard composite powder, and recording as standard flow energy TFEs; determining the flow energy of composite powder to be evaluated, calculating percentage difference P·Vds between TFEd and TFEs, and evaluating mixing uniformity of composite powder according to P·Vds.

Abstract: An evaluation method of mixing uniformity of composite powder includes: determining raw materials of composite powder to be evaluated and mass ratio; mixing to obtain multiple standard composite powder with different mixing time; determining flow energy of each standard composite powder; analyzing the flow energy of multiple standard composite powders by significant difference method, determining at least 3 consecutive standard composite powders with no significant difference in flow energy according to mixing time from small to large, defining as uniform-mixed standard composite powder, calculating average value of flow energy of uniform-mixed standard composite powder, and recording as standard flow energy TFEs; determining the flow energy of composite powder to be evaluated, calculating percentage difference P·Vds between TFEd and TFEs, and evaluating mixing uniformity of composite powder according to P·Vds.

Abstract: In some cases, lower quality, large scale training data can be automatically generated by automatic labeling. The generated training data can be used to pre-train a machine learning model. For instance, the model can be a model for detection of verbal harassment. Parameters of the pre-trained model can be refined or updated using another one or more higher-quality sets of training data, with which the model can be subsequently trained.

Abstract: In some cases, a verbal harassment detection system may use machine learning models to detect verbal harassment in real-time or near real-time. The system may receive an audio segment comprising a portion of audio captured by a microphone located within a vehicle. Further, the system may convert the audio segment to a text segment. The system may provide at least the text segment to a prediction model associated with verbal harassment detection to obtain a harassment prediction. Further, the system may provide the audio segment to an emotion detector to obtain a detected emotion of a speaking user that made an utterance included in the audio segment. Based at least in part on the harassment prediction and the detected emotion, the system may automatically, and without user intervention, determine whether a user is being harassed.

Abstract: In some cases, one or more heuristics can be automatically generated using a small dataset of segments previously labeled by one or more domain experts. The generated one or more heuristics along with one or more patterns can be used to assign training labels to a large unlabeled dataset of segments. A subset of segments representing an occurrence of verbal harassment can be selected using the assigned training labels. Randomly selected segments can be used as being indicative of a non-occurrence of verbal harassment. The selected subset of segments and randomly selected segments can be used to train one or more machine learning models for verbal harassment detection.

Abstract: A probe head includes a probe seat having upper, middle and lower dies, an electrically conductive layer inside the probe seat, a first spring probe penetrating through the probe seat, and at least two shorter second spring probes penetrating through the lower die in a way that top ends of the second spring probes are located inside the probe seat and abutted against the electrically conductive layer. Another probe head includes the aforesaid probe seat, an electrically conductive layer partially inside the probe seat and partially outside the probe seat, a first spring probe penetrating through the probe seat, and a shorter second spring probe penetrating through the lower die in a way that a top end of the second spring probe is located inside the probe seat and abutted against the electrically conductive layer. As such, fine pitch requirement and different high frequency testing requirements are fulfilled.

Abstract: In some cases, lower quality, large scale training data can be automatically generated by automatic labeling. The generated training data can be used to pre-train a machine learning model. For instance, the model can be a model for detection of verbal harassment. Parameters of the pre-trained model can be refined or updated using another one or more higher-quality sets of training data, with which the model can be subsequently trained.

Abstract: In some cases, one or more heuristics can be automatically generated using a small dataset of segments previously labeled by one or more domain experts. The generated one or more heuristics along with one or more patterns can be used to assign training labels to a large unlabeled dataset of segments. A subset of segments representing an occurrence of verbal harassment can be selected using the assigned training labels. Randomly selected segments can be used as being indicative of a non-occurrence of verbal harassment. The selected subset of segments and randomly selected segments can be used to train one or more machine learning models for verbal harassment detection.

Abstract: Certain aspects disclosed herein improve on inappropriate behavior detection by applying machine-learning techniques to identify inappropriate behavior in real-time, or near real-time. For example, one or more patterns can be generated that represent possible incidences of inappropriate behavior, such as a request for a user's contact information. Audio segments captured by a wireless device in a vehicle can be converted into text using an automatic speech recognition system, and an adaptation model can apply the pattern(s) to the text to determine portions of the text that match at least one pattern and portions of the text that do not match any pattern, with the adaptation model labeling the text portions accordingly. The adaptation model can then pre-train a text classification model with the labeled text portions. The adaptation model obtains manually labeled data points and re-trains or updates the pre-trained text classification model using the manually labeled data points.

Abstract: In some cases, a verbal harassment detection system may use machine learning models to detect verbal harassment in real-time or near real-time. The system may receive an audio segment comprising a portion of audio captured by a microphone located within a vehicle. Further, the system may convert the audio segment to a text segment. The system may provide at least the text segment to a prediction model associated with verbal harassment detection to obtain a harassment prediction. Further, the system may provide the audio segment to an emotion detector to obtain a detected emotion of a speaking user that made an utterance included in the audio segment. Based at least in part on the harassment prediction and the detected emotion, the system may automatically, and without user intervention, determine whether a user is being harassed.

Abstract: A probe head includes a probe seat having upper, middle and lower dies, an electrically conductive layer inside the probe seat, a first spring probe penetrating through the probe seat, and at least two shorter second spring probes penetrating through the lower die in a way that top ends of the second spring probes are located inside the probe seat and abutted against the electrically conductive layer. Another probe head includes the aforesaid probe seat, an electrically conductive layer partially inside the probe seat and partially outside the probe seat, a first spring probe penetrating through the probe seat, and a shorter second spring probe penetrating through the lower die in a way that a top end of the second spring probe is located inside the probe seat and abutted against the electrically conductive layer. As such, fine pitch requirement and different high frequency testing requirements are fulfilled.

Abstract: Differing from conventional directional coupling device being implemented on a coin-like planar board, the present invention stacks a bottom substrate, at least one phase retarding unit, at least one reference ground unit, a coupled circuit layer, a main circuit layer, and a top substrate to form a miniature directional coupling device. Because this miniature directional coupling device not occupies too much circuit area when being applied in a mobile communication product, the miniature directional coupling device can meet the requirements of light weight and compact size demanded by high-technology mobile communications for the electronic components. It is worth explaining that, since the said phase retarding unit consists of many end-to-end connected transmission wires, engineers skilled in designing microwave circuit are able to carry out the modulation of coupling flatness of the miniature directional coupling device by changing a total length of the end-to-end connected transmission wires.

Abstract: Differing from conventional directional coupling device being implemented on a coin-like planar board, the present invention stacks a bottom substrate, at least one phase retarding unit, at least one reference ground unit, a coupled circuit layer, a main circuit layer, and a top substrate to form a miniature directional coupling device. Because this miniature directional coupling device not occupies too much circuit area when being applied in a mobile communication product, the miniature directional coupling device can meet the requirements of light weight and compact size demanded by high-technology mobile communications for the electronic components. It is worth explaining that, since the said phase retarding unit consists of many end-to-end connected transmission wires, engineers skilled in designing microwave circuit are able to carry out the modulation of coupling flatness of the miniature directional coupling device by changing a total length of the end-to-end connected transmission wires.