Abstract: A workpiece is measured using multiple-pass spectroscopic ellipsometry and multi-wavelength Raman spectroscopy, which may be performed in the same system. These measurements are combined to form combined measured data. A stress measurement of the workpiece is determined using the combined measured data. The stress measurement can be determined using a model or a machine learning algorithm.

Abstract: A metrology system may include a spectroscopic metrology sub-system and a controller, the controller including one or more processors configured to execute program instructions configured to cause the one or more processors to: generate a tool-induced shift (TIS) model of a training sample by the metrology sub-system comprising: receiving training data from metrology measurements of the training sample, the training data comprising spectral data associated with at least one off-diagonal Mueller matrix element generated by one or more first measurements of the training sample at a first azimuthal angle and one or more second measurements of the training sample at a second azimuthal angle, deriving overlay spectra data and TIS spectra data from the training data, decomposing the overlay spectra data and the TIS spectra data, and inferring a TIS signature for the training sample; and to remove the TIS signature from a test sample.

Abstract: Apparatus, methods, and computer program products for wireless communication are provided. An example method may include receiving, from a network entity, an LTM configuration. The example method may further include receiving, from the network entity, an LTM cell switch command associated with the LTM configuration. The example method may further include transmitting, to the network entity, a RLF report based on a failure of an LTM cell switch based on the LTM cell switch command or an RLF after the LTM cell switch based on the LTM cell switch command, the RLF report including information associated with the LTM configuration or the LTM cell switch command.

Abstract: Apparatus, methods, and computer program products for wireless communication are provided. An example method may include receiving, from a network entity, an LTM configuration. The example method may further include receiving, from the network entity, an LTM cell switch command associated with the LTM configuration. The example method may further include transmitting, to the network entity, a RLF report based on a failure of an LTM cell switch based on the LTM cell switch command or an RLF after the LTM cell switch based on the LTM cell switch command, the RLF report including information associated with the LTM configuration or the LTM cell switch command.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may measure quality of experience (QoE) metrics and transmit a QoE report that is formatted to be readable by a base station, such that a base station may receive the QoE report and independently perform adjustments associated with the service being utilized by the UE. A UE may receive a configuration message that includes a first configuration for reporting QoE measurements to a base station and a second configuration for reporting QoE measurements to a QoE server. The UE may measure one or more QoE metrics in accordance with the configuration message, and generate a first report for the base station based on the QoE measurements and the first configuration. Upon generating the report, the UE may transmit the first report to the base station in accordance with the first configuration.

Abstract: Methods, systems, and devices for wireless communications are described. A UE may be configured with a machine learning model by a core network to perform analytics, training, or inferences. The UE may indicate capability information to a core network entity, including a list of machine learning models supported at the UE. A centralized core network entity may manage different machine learning models and may send information for a machine learning model to the UE, such as through another core network entity. The UE or the core network may initiate the configuration. For example, the UE may request to be configured with a machine learning model. The core network may send control signaling that indicates a configuration for the machine learning model to the UE. The UE may perform analytics based on the machine learning model.

Abstract: A method for wireless communication by a user equipment (UE) includes receiving, from a network node, a first message indicating one or more parameters associated with a limited mobility zone within a coverage area of one or more cells. The method also includes transmitting, to the network node, a second message indicating the UE is within the limited mobility zone in accordance with determining the UE is within the limited mobility zone in accordance with the one or more parameters. The method further includes receiving, from the network node, a third message including first paging information in accordance with a first paging configuration associated with the limited mobility zone.

Abstract: Certain aspects of the present disclosure provide techniques for data collection for non-terrestrial networks (NTN). One aspect provides a method for wireless communications by a user equipment (UE). The method generally includes transmitting an indication of a capability of the UE to connect to a network via both terrestrial network (TN) cells and non-terrestrial network (NTN) cells and transmitting one or more data collection reports in accordance with the indicated capability.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from a primary cell or a primary secondary cell (PSCell), configuration information for a conditional PSCell addition or change (CPAC), the configuration information indicating configurations for one or more candidate PSCells. The UE may detect a failure, associated with the conditional PSCell addition or change, associated with a first target PSCell from the one or more candidate PSCells. The UE may perform an action to recover the CPAC based at least in part on detecting the failure. Numerous other aspects are described.

Abstract: Methods, systems, and devices for wireless communications are described. In some examples, a user equipment (UE) may support dual connectivity. That is, the UE may establish communications with a master node and a secondary node of a wireless communications network. The master node may correspond to a master cell group (MCG) and the secondary node may correspond to a secondary cell group (SCG). In some examples, as described herein, the UE operating in dual connectivity may collect data for optimization of the wireless communications network or upon detecting a failure associated with the master cell group or the SCG and transmit the collected data to a network entity (e.g., one of the master node or the secondary node), where the collected data is based on the SCG being in a deactivated state. In some examples, upon receiving the collected data, the network entity may attempt to recover from the failure.

Abstract: Methods and systems for performing spectroscopic ellipsometry (SE) measurements of semiconductor structures based on data sets resolved in wavelength, azimuth angle, and angle of incidence are presented herein. In some embodiments, machine learning based measurement models are trained to infer estimated values of one or more parameters of interest characterizing a structure under measurement based on SE measurement data resolved in wavelength, azimuth angle, and angle of incidence. In some other embodiments, regression is performed on a physics based measurement model to estimate values of one or more parameters of interest characterizing a structure under measurement. A dispersive element in the collection beam path disperses collected light across the active surface of a detector to resolve collected light according to wavelength and one angular dimension. Furthermore, multiple images are collected by the detector to resolve collected light across the other angular dimension.

Abstract: A metrology system may include a dual frequency comb source providing a first comb beam with a first repetition rate and a second comb beam with a second repetition rate, a beamsplitter to generate one or more dual frequency comb illumination beams from the first comb beam and the second comb beam, and a beam combiner to form a dual frequency comb illumination beam from the first comb beam and the second comb beam. The system may further include an illumination sub-system to illuminate a sample with the dual frequency comb illumination beam through an objective lens, a collection sub-system to collect sample light from the sample with the objective lens, and a detector to capture a radio-frequency signal based on the sample light. The system may further extract spectral measurement data associated with the sample from the radio-frequency signal and generate metrology measurements based on the spectral measurement data.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may obtain an indication to perform a selective secondary cell group activation or a subsequent conditional handover associated with a subsequent conditional primary cell change, the subsequent conditional handover associated with the subsequent conditional primary cell change being performed after a previous conditional handover associated with a previous conditional primary cell change. The UE may initiate the selective secondary cell group activation or the subsequent conditional handover. The UE may detect a failure occurrence associated with the selective secondary cell group activation or the subsequent conditional handover.

Abstract: This disclosure provides systems, methods, and devices for wireless communication that support AI model-based enhancements for RRC IDLE and INACTIVE state operations. In a first aspect, a method of wireless communication includes receiving, by a wireless communication device, artificial intelligence (AI) model configuration information for IDLE/INACTIVE state procedures; retrieving, by the wireless communication device, an AI model for IDLE/INACTIVE state procedures based on the AI model configuration information; and performing, by the wireless communication device, one or more IDLE/INACTIVE state procedures based on the AI model. Other aspects and features are also claimed and described.

Abstract: Certain aspects of the present disclosure provide techniques for data collection for non-terrestrial networks (NTN). One aspect provides a method for wireless communications by a user equipment (UE). The method generally includes transmitting an indication of a capability of the UE to connect to a network via both terrestrial network (TN) cells and non-terrestrial network (NTN) cells and transmitting one or more data collection reports in accordance with the indicated capability.

Abstract: A source network node and a UE may obtain at least one mobility related prediction associated with the UE or at least one target network node, the at least one mobility related prediction being derived by at least one neural network, and the source network node may handover the UE from the source network node to the at least one target network node based on the at least one mobility related prediction. The target network node may receive the handover request, obtain at least one mobility related prediction associated with the UE or the target network node, and output for transmission a handover request ACK, the handover request ACK based at least in part on the at least one mobility related prediction.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a first control message that includes a quality of experience (QoE) measurement configuration that indicates one or more QoE measurement references for the EE to perform and one or more indicator flags requesting a start indication and an end indication and an associated minimization of drive test (MDT) configuration to be aligned with the one or more QoE measurement references. The EE may transmit the start or end indication associated with the measurement session based on the indicator flag being set for the measurement session, and may receive a second control message that includes the MDT configuration for the EE to apply in addition to the QoE measurement references.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive first system information indicative of a first synchronization signal block (SSB) configuration, the first SSB configuration indicating a plurality of SSBs comprising at least one deactivated SSB. The UE may receive, based on a reactivation of the at least one deactivated SSB, second system information, the second system information omitting a second SSB configuration. Numerous other aspects are described.

Abstract: Methods, systems, and devices for wireless communication are described. A network entity may configure a user equipment (UE) to report applicability information associated with artificial intelligence (AI) and/or machine learning (ML) functionalities and/or models. The applicability information indicates an applicability of a functionality and/or model per AI/ML-enabled feature or feature group. Based on the applicability information and a handover of the UE from a source network entity to a target network entity, the target network entity may configure a functionality and/or model. Additionally, or alternatively, the UE may be configured with a reference configuration and one or more additional configurations corresponding to respective AI/ML functionalities and/or models. The UE may apply an additional configuration based on a handover to the target network entity, the additional configuration satisfying an applicability condition, or both.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may transmit, to a network node, a request for network assisted information associated with one or more of a machine learning (ML) model or analytics, the ML model or the analytics being associated with an application layer communication that is associated with the UE. The UE may receive, from the network node, one or more of the ML model or the analytics. Numerous other aspects are described.