Abstract: Methods and systems for performing spectroscopic reflectometry based measurements of deep, large pitch semiconductor structures at high throughput are presented herein. A multi-step regression algorithm employs a non-periodic electromagnetic (EM) solver, i.e., an EM solver that does not rely on a Fourier based analysis. The multi-step regression algorithm utilizes both incoherent and coherent reflections resulting in faster and more accurate estimates of parameters of interest, including the depth of deep trench or deep hole structures. The coherent sum of reflection coefficients captures height differences among sub-structures of a structure under measurement, whereas an incoherent sum of reflection coefficients largely ignores the height differences.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, an enhanced distributed unit (eDU) may transmit, to a first network node, a first configuration relating to a first cell. The eDU may receive, from a second network node, a second configuration for a user equipment (UE), the second configuration including information relating to the first cell. The eDU may communicate with the UE in accordance with the second configuration. The eDU may receive, from the first network node, a cell characteristic relating to a second cell. The eDU may transmit, to the UE, a third configuration, wherein the third configuration is based at least in part on at least one of the first configuration or the second configuration. Numerous other aspects are described.

Abstract: Methods, systems, and devices for wireless communications are described. In some examples, a wireless communications system may support machine learning and may configure a user equipment (UE) for machine learning. The UE may transmit, to a base station, a request message that includes an indication of a machine learning model or a neural network function based at least in part on a trigger event. In response to the request message, the base station may transmit a machine learning model, a set of parameters corresponding to the machine learning model, or a configuration corresponding to a neural network function and may transmit an activation message to the UE to implement the machine learning model and the neural network function.

Abstract: Certain aspects of the present disclosure provide techniques for wireless communications by a user equipment (UE), generally including obtaining a set of key performance indicators (KPIs) for a machine learning (ML) model running on the UE and transmitting, to an entity associated with the ML model, a report including an aggregation of the KPIs and additional performance feedback for the ML model.

Abstract: A network node obtains, from a core network component, a quality of experience (QoE) configuration associated with a high speed dedicated network (HSDN) indication and outputs the QoE configuration for a user equipment (UE) based on the UE being camped on an HSDN cell or based on a mobility condition of the UE. A UE receives, from a network node, a QoE configuration associated with an HSDN indication and collects QoE information based on at least one of an HSDN status of a cell or a mobility condition of the UE.

Abstract: Methods, systems, and devices for wireless communications are described. The described techniques may enable a distributed unit (DU) to transmit backhaul signaling to a central unit (CU) indicating duplex information. For example, the duplex information may indicate an active duplex mode (e.g., subband full-duplex, subband half-duplex, or full-duplex), frequency or time information related to one or more duplex modes, or a random access channel (RACH) configuration that is specific to one or more duplex modes. In some examples, the CU may forward the duplex information to another CU or DU. Additionally, or alternatively, one or more network entities may transmit the duplex information to a user equipment (UE). For example, the one or more network entities may indicate multiple duplex configurations to the UE, and may transmit signaling activating one of the multiple duplex configurations.

Abstract: Certain aspects of the present disclosure provide method of wireless communications by a user equipment (UE), generally including obtaining, from a network entity, a performance report for a machine learning (ML) model running on at least one of the UE or a network entity and participating in a change to the ML model based on the performance report.

Abstract: Methods and systems for measuring structural parameters characterizing a measurement target based on changes in measurement signal values due to one or more perturbations of an effective illumination angle of incidence on the measurement target are presented herein. In some examples, a measurement model estimates values of the structural parameters based on the changes in measurement signal values. In some examples, at least one derivative of detected measurement signals with respect to effective illumination angle is determined, and values of the structural parameters are estimated based on the at least one derivative. In some examples, values of one or more tunable measurement model parameters are estimated based on at least one derivative. In some examples, the fitting performance of a measurement model is quantified based on measurements performed at both unperturbed and perturbed orientations of a structure under measurement with respect to the illumination beam.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from at least one of a master node (MN) or a secondary node (SN), one or more radio access network visible quality of experience (RVQoE) configurations, wherein the UE is operating in a dual connectivity mode with the MN and the SN. The CE may transmit, to at least one of the MN or the SN, at least one RVQoE measurement report based at least in part on the one or more RVQoE configurations. Numerous other aspects are described.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may perform a mobility operation in or to a non-terrestrial network (NTN). The UE may transmit a report indicating a success or failure of the mobility operation, wherein the report is associated with a timing difference of the mobility operation satisfying a threshold associated with at least one of the NTN or the mobility operation. Numerous other aspects are described.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may determine, by a UE radio resource control (RRC) layer of the UE, that a quality of experience (QoE) reporting is paused based at least in part on a network overload, wherein buffered QoE data for a QoE configuration is stored at the UE when the QoE reporting is paused. The UE may receive, at the UE RRC layer from a UE application layer of the UE, an attention command that indicates a QoE measurement session stop indication for the QoE configuration. The UE may transmit, based at least in part on the attention command, the buffered QoE data without discarding the buffered QoE data stored at the UE. Numerous other aspects are described.

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 exchange capability information with a core network related to machine learning models. For example, the UE may indicate a list of machine learning models supported at the UE to a core network entity, and the UE may receive an indication of a list of machine learning models supported at the 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 indicating a configuration for the machine learning model to the UE. The UE may perform analytics based on the machine learning model.

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: 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: 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: 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.