Abstract: In one example in accordance with the present disclosure, a modular computing component is described. The modular computing component includes a first terminal to connect the modular computing component to at least one of a host computing device and another modular computing component. Controller memory of the modular computing component stores information relating to at least one of build level information, revision level information, and generation level information. A controller of the modular computing component transmits the at least one of build level information, revision level information, and generation level information to the host computing device.

Abstract: Example implementations relate to balancing a power load among USB ports. For example, an apparatus according to the present disclosure, may include a plurality of USB ports, and an embedded controller coupled to the plurality of USB ports. The embedded controller may determine that a first device is coupled to a USB port of the plurality of USB ports, and determine a power draw of the first device relative to a type of the USB port. The embedded controller may balance a power load among a remainder of the plurality of USB ports based on the power draw of the first device relative to the type of the USB port.

Abstract: A method of improving at least one of quality and yield of a physical process comprises: obtaining values, from respective performances of the physical process, for a plurality of variables associated with the physical process; determining at least one Gaussian mixture model (GMM) representing the values for the variables for the performances of the physical process; based at least in part on the at least one GMM, computing at least one anomaly score for at least one of the variables for at least one of the performances of the physical process; based on the at least one anomaly score, identifying the at least one of the performances of the physical process as an outlier; and, based at least in part on the outlier identification, modifying the at least one of the variables for one or more subsequent performances of the physical process.

Abstract: A method for process control using predictive long short term memory includes obtaining historical post-process measurements taken on prior products of the manufacturing process; obtaining historical in-process measurements taken on prior workpieces during the manufacturing process; training a neural network to predict each of the historical post-process measurements, in response to the corresponding historical in-process measurements and preceding historical post-process measurements; obtaining present in-process measurements on a present workpiece during the manufacturing process; predicting a future post-process measurement for the present workpiece, by providing the present in-process measurements and the historical post-process measurements as inputs to the neural network; and adjusting at least one controllable variable of the manufacturing process in response to the prediction of the future post-process measurement.

Abstract: Anomaly detection and remedial recommendation techniques for improving the quality and yield of microelectronic products are provided. In one aspect, a method for quality and yield improvement via anomaly detection includes: collecting time series sensor data during individual steps of a semiconductor manufacturing process; calculating anomaly scores for each of the individual steps using a predictive model; and implementing changes to the semiconductor manufacturing process based on the anomaly scores. A system for quality and yield improvement via anomaly detection is also provided.

Abstract: Methods and systems for determining an optimized set point for a manufacturing apparatus are described. In an example, a processor may receive observed data from the manufacturing apparatus. The observed data may include data collected by the manufacturing apparatus based on at least one resolution. The processor may generate feature data based on the observed data. The processor may determine a first model and a second model based on the feature data. The first model may relate to a first prediction of a key performance indicator of the manufacturing apparatus in a first amount of future time. The second model may relate to a second prediction of the key performance indicator of the manufacturing apparatus in a second amount of future time. The processor may determine the optimized set point based on an objective relating to the first model and based on a constraint relating to the second model.

Abstract: In an approach for compressing a neural network, a processor receives a neural network, wherein the neural network has been trained on a set of training data. A processor receives a compression ratio. A processor compresses the neural network based on the compression ratio using an optimization model to solve for sparse weights. A processor re-trains the compressed neural network with the sparse weights. A processor outputs the re-trained neural network.

Abstract: Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

Abstract: A computer-implemented method for controlling a manufacturing process. A non-limiting example of the computer-implemented method includes using a processor to perform discretization modeling of a continuous probability distribution to yield a prediction of a future probability distribution. Next, the method uses the processor to impose a smoothness condition on the predicted probability distribution. The method using the processor to perform a multi-step forecast of the probability distribution to create a predicted probability density function. The method uses the predicted probability density function as an input to a process control system and uses the processor to control a process using the predicted probability density function.

Abstract: An apparatus may include a basic input/output system (BIOS) coupled to a controller. A communication port may be coupled to the controller. The controller may determine that the communication port has entered a locked state, send a first signal to the communication port to power off a bus associated with the communication port for a threshold period of time, and send a second signal to the communication port to power on the bus associated with the communication port in response to expiration of the threshold period of time.

Abstract: An example system includes a processor. The system also includes a peripheral interface that includes a controller communicatively coupled to the processor. The controller is to request information from a plurality of devices connected to the peripheral interface prior to the processor requesting the information. The controller is to provide the information to the processor.

Abstract: An example peripheral device includes a module interface to receive power and data communication from a computing device. The peripheral device also includes an attachment tab to affix the peripheral device to a lower side of the computing device. The peripheral device further includes a latch to control an engagement of the attachment tab with the computing device. The peripheral device further includes a sensing circuit to detect a change in position of the latch. The peripheral device further includes a controller to, in response to detecting the latch moving from a locked position to an unlocked position, indicate a hot unplug prediction to the computing device via the module interface.

Abstract: An example method includes receiving an indication of a first level of authentication for an electronic device, the first authentication being associated with a first authentication device associated with the user; receiving an indication of a second level of authentication for the electronic device, the second authentication being associated with a second authentication device associated with the user, the second authentication device being different from the first authentication device; and upon receiving the indication of at least the first level of authentication and the second level of authentication, allow access to the electronic device.

Abstract: Example implementations relate to transmitting signals via USB ports. For example, a system according to the present disclosure, may include a host module including a plurality of USB ports, a first expansion module, and a second expansion module. The first expansion module may include a first USB port and a second USB port. The first expansion module may receive a signal from the host module at a first USB port, and direct the signal to a second USB port. The first expansion module may transmit the signal to a second expansion module via a second USB port.

Abstract: Methods and systems for determining an optimized set point for a manufacturing apparatus are described. In an example, a processor may receive observed data from the manufacturing apparatus. The observed data may include data collected by the manufacturing apparatus based on at least one resolution. The processor may generate feature data based on the observed data. The processor may determine a first model and a second model based on the feature data. The first model may relate to a first prediction of a key performance indicator of the manufacturing apparatus in a first amount of future time. The second model may relate to a second prediction of the key performance indicator of the manufacturing apparatus in a second amount of future time. The processor may determine the optimized set point based on an objective relating to the first model and based on a constraint relating to the second model.

Abstract: An example intrusion detection system for a computer includes: an ambient light sensor to detect an increase in ambient light indicative of a housing of the computer being opened; and a super input/output integrated circuit (SIO) to receive a signal from the ambient light sensor indicating that the housing of the computer has been opened.

Abstract: An example computing device includes a module interface to communicate with a peripheral device. The computing device also includes a hot swapping prediction circuit to detect a physical movement of the computing device and to generate a hot swapping prediction signal based on the detected physical movement. The computing device further includes a processor coupled to the hot swapping circuit. The processor is to, in response to detecting the hot swapping prediction signal from the hot swapping circuit, change a parameter of a peripheral device detection operation to be executed by an operating system of the computing device.

Abstract: Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

Abstract: Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

Abstract: Example implementations relate to balancing a power load among USB ports. For example, an apparatus according to the present disclosure, may include a plurality of USB ports, and an embedded controller coupled to the plurality of USB ports. The embedded controller may determine that a first device is coupled to a USB port of the plurality of USB ports, and determine a power draw of the first device relative to a type of the USB port. The embedded controller may balance a power load among a remainder of the plurality of USB ports based on the power draw of the first device relative to the type of the USB port.