Abstract: An electronic device manufacturing system configured to obtain sensor data associated with a deposition process performed in a process chamber to deposit a film stack on a surface of a substrate. The film stack can include a known film pattern and an unknown film pattern. The manufacturing system is further configured to input the sensor data into a first trained machine-learning model to obtain a first output value of the first trained machine-learning model. The first output value can be associated with the known film pattern. The manufacturing system is further configured to input the first output value into a second trained machine-learning model to obtain a second output value of the second trained machine-learning model. The second output value can be indicative of metrology data of the known film pattern.

Abstract: An electronic device manufacturing system configured to obtain, by a processor, sensor data associated with a substrate manufacturing process performed in a process chamber. The sensor data is provided, as input data, to a machine learning model. The machine learning trained uses a weighted feature that reflects a relationship between one or more variables related to an initial feature and a characteristic sequence that defines a relationship between two or more manufacturing parameters. An output value of the machine learning model is obtained, the output value being indicative of metrology data.

Abstract: An electronic device manufacturing system configured to adjust a virtual knob associated with a trained machine learning model. The virtual knob is configured to adjust a representative value of the trained machine learning model to control a performance of the trained machine learning model. Sensor data is provided as input to the trained machine learning model, the sensor data being associated with a substrate manufacturing process performed on a substate. An output value of the modified machine learning model is obtained, the output value being indicative of metrology data.

Abstract: An electronic device manufacturing system configured to receive, by a processor, input data reflecting a feature related to a manufacturing process of a substrate. The manufacturing system is further configured to generate a characteristic sequence defining a relationship between at least two manufacturing parameters, and determine a relationship between one or more variables related to the feature and the characteristic sequence. The manufacturing system is further configured to determine a weight based on the determined relationship and apply the weight to the feature. The manufacturing system is further configured to train a machine-learning model in view of the weighted feature.

Abstract: An electronic device manufacturing system configured to receive, by a processor, input data reflecting a feature related to a manufacturing process of a substrate. The manufacturing system is further configured to train a machine-learning model based on the input data reflecting the feature. The manufacturing system is further configured to modify the machine-learning model in view of the virtual knob for the feature.

Abstract: A rotary seat including a base includes an outer surface, and a composite material layer attached to at least a part of the outer surface. The base also includes a recess for accommodating a turntable and including a first opening on the outer surface. The material of the composite material layer includes fibers and a resin. Therefore, the rotary seat may be more lightweight. A rotary table is also provided and includes the rotary seat, a driving device which drives the rotary seat to rotate, and a turntable which is rotatably disposed in the recess of the base.

Abstract: An electronic device manufacturing system configured to receive, by a processor, input data reflecting a feature related to a manufacturing process of a substrate. The manufacturing system is further configured to generate a characteristic sequence defining a relationship between at least two manufacturing parameters, and determine a relationship between one or more variables related to the feature and the characteristic sequence. The manufacturing system is further configured to determine a weight based on the determined relationship and apply the weight to the feature. The manufacturing system is further configured to train a machine-learning model in view of the weighted feature.

Abstract: An electronic device manufacturing system configured to receive, by a processor, input data reflecting a feature related to a manufacturing process of a substrate. The manufacturing system is further configured to train a machine-learning model based on the input data reflecting the feature. The manufacturing system is further configured to modify the machine-learning model in view of the virtual knob for the feature.

Abstract: An electronic device manufacturing system configured to obtain sensor data associated with a deposition process performed in a process chamber to deposit a film stack on a surface of a substrate. The film stack can include a known film pattern and an unknown film pattern. The manufacturing system is further configured to input the sensor data into a first trained machine-learning model to obtain a first output value of the first trained machine-learning model. The first output value can be associated with the known film pattern. The manufacturing system is further configured to input the first output value into a second trained machine-learning model to obtain a second output value of the second trained machine-learning model. The second output value can be indicative of metrology data of the known film pattern.

Abstract: A rotary seat including a base includes an outer surface, and a composite material layer attached to at least a part of the outer surface. The base also includes a recess for accommodating a turntable and including a first opening on the outer surface. The material of the composite material layer includes fibers and a resin. Therefore, the rotary seat may be more lightweight. A rotary table is also provided and includes the rotary seat, a driving device which drives the rotary seat to rotate, and a turntable which is rotatably disposed in the recess of the base.

Abstract: A fingerprint sensing system has a fingerprint sensing device and a power supply circuit. The fingerprint sensing device has a high-voltage input terminal and a low-voltage input terminal. During scan phases of the fingerprint sensing device, the power supply circuit provides a first voltage to the high voltage input terminal and provides a second voltage to the low voltage input terminal. During the read phases of the fingerprint sensing device, the power supply circuit provides a third voltage to the high voltage input terminal and provides a fourth voltage to the low voltage input terminal. A first voltage difference is between the first voltage and the second voltage. A second voltage difference is between the third voltage and the fourth voltage. The first voltage difference is greater than the second voltage difference.

Abstract: A fingerprint sensing system has a fingerprint sensing device and a power supply circuit. The fingerprint sensing device has a high-voltage input terminal and a low-voltage input terminal. During scan phases of the fingerprint sensing device, the power supply circuit provides a first voltage to the high voltage input terminal and provides a second voltage to the low voltage input terminal. During the read phases of the fingerprint sensing device, the power supply circuit provides a third voltage to the high voltage input terminal and provides a fourth voltage to the low voltage input terminal. A first voltage difference is between the first voltage and the second voltage. A second voltage difference is between the third voltage and the fourth voltage. The first voltage difference is greater than the second voltage difference.

Abstract: A sensing method of a fingerprint sensor includes the following steps: (a) applying a first voltage, a second voltage and a third voltage to a first node connected to an electrode plate to be measured, a second node disconnected from the first node, and a conductor adjacent to the electrode plate to be measured, respectively; (b) providing a first finger drive voltage for a finger; (c) stopping applying the first, second and third voltages to the first node, the second node and the conductor, respectively; (d) after step (c), applying a fourth voltage to the conductor, and connecting the first node to the second node; (e) after steps (b) and (c), providing a second finger drive voltage for the finger; and (f) after steps (d) and (e), obtaining a measurement result of the electrode plate to be measured according to a signal on the second node.

Abstract: A sensing method of a fingerprint sensor includes the following steps: (a) applying a first voltage, a second voltage and a third voltage to a first node connected to an electrode plate to be measured, a second node disconnected from the first node, and a conductor adjacent to the electrode plate to be measured, respectively; (b) providing a first finger drive voltage for a finger; (c) stopping applying the first, second and third voltages to the first node, the second node and the conductor, respectively; (d) after step (c), applying a fourth voltage to the conductor, and connecting the first node to the second node; (e) after steps (b) and (c), providing a second finger drive voltage for the finger; and (f) after steps (d) and (e), obtaining a measurement result of the electrode plate to be measured according to a signal on the second node.