12 results about "Virtual metrology" patented technology

The invention discloses an on-line extraction self-optimization method for acanthopanax sessiliflorus flavone based on reinforcement learning. According to the method, multi-source data in the extraction process is collected in real time, and a fusion feature vector is generated after preprocessing; establishing a mapping relation from the feature vector to the contents of rutin, quercetin, kaempferol and hyperoside by using an offline test sample, and constructing a virtual measurer capable of outputting a target flavone composition vector and uncertainty thereof; constructing a safety domain boundary according to a solvent flash point temperature, an equipment tolerance temperature upper limit and a component thermal degradation temperature threshold, and calculating a time-varying safety constraint threshold in combination with uncertainty; performing linkage optimization on the temperature, the ethanol volume fraction, the solid-to-liquid ratio, the soaking time and the circulating flow by adopting a reinforcement learning strategy, and comprehensively considering flavone composition deviation and solvent energy consumption by a reward function; and finally, outputting the optimized parameters to extraction equipment for execution. According to the method, online, lossless and self-adaptive process optimization control is realized, inter-batch fluctuation is remarkably reduced, and the resource utilization efficiency is improved.

The invention discloses a virtual measurement method and device, equipment, a storage medium and a product, and relates to the technical field of computers, and the virtual measurement method comprises the steps that multi-time-scale abstract processing is conducted on process parameters and production data sequences obtained online, and a multi-scale time sequence group is obtained; based on a preset virtual measurement model, virtual measurement is carried out on the multi-scale time sequence group, a key parameter measurement result is obtained, and the virtual measurement model is obtained by adopting a reinforcement learning mode to carry out virtual measurement training on a preset time sequence data sample. According to the method, the multi-scale time sequence group of the online time sequence characteristics is extracted, virtual measurement is performed on the online time sequence characteristics by using the trained virtual measurement model, the key parameter measurement result of the composite index is obtained, accurate prediction of the composite index is realized, and the accuracy of the prediction result is improved. Therefore, the problem that comprehensive monitoring of the composite indexes is difficult to achieve in precise laser cutting through a traditional method is solved, and the prediction accuracy is improved.

The invention relates to the technical field of semiconductor manufacturing quality control, in particular to a space-time coupling virtual measurement intelligent labeling method based on feature space geometry, which comprises the following steps: S1, acquiring multi-dimensional sensor time sequence data of semiconductor manufacturing equipment as a labeling sample set, and performing manifold value evaluation on the labeling sample set; s2, establishing an adaptive time weight model based on data complexity, establishing a dynamic space weight model based on local sparseness based on an adaptive kernel function, combining the adaptive time weight model, the dynamic space weight model and manifold value evaluation, and defining a time-space coupling value score; s3, taking the time-space coupling value score as an input, and establishing a three-layer optimization architecture to realize a globally optimal sample combination; s4, combining the samples selected by each cluster to form a final label set, performing quality evaluation on the final label set, and generating standardized label output according to a quality evaluation result; and an accurate value basis is provided for subsequent intelligent selection.

A specialized machine learning model configured to minimize and / or eliminate the need for motorized hardware to block higher order diffracted radiation from corrupting alignment measurements in semiconductor manufacturing metrology processes. For example, a radiation source illuminates a metrology target in a layer of a patterned substrate with radiation, the metrology target diffracts the radiation. A radiation sensor outputs phase data and intensity data of the diffracted radiation. The phase data includes different energies associated with different diffraction orders of the diffracted radiation. The model is configured to determine a subset of the intensity data associated with positive first order diffracted radiation and negative first order diffracted radiation incident on the radiation sensor based on the phase data, and determine an intensity difference or intensity imbalance between the positive first order diffracted radiation and the negative first order diffracted radiation based on the subset of the intensity data. An alignment is determined based on the intensity imbalance.

The present application relates to the technical field of semiconductor manufacturing quality control, and specifically relates to a space-time coupling virtual metrology intelligent labeling method based on feature space geometry, comprising the following steps: S1: obtaining multi-dimensional sensor time series data of a semiconductor manufacturing device as a labeling sample set, and performing manifold value evaluation on the labeling sample set; S2: establishing an adaptive time weight model based on data complexity, establishing a dynamic space weight model based on local sparsity based on an adaptive kernel function, combining the adaptive time weight model, the dynamic space weight model and the manifold value evaluation, and defining a space-time coupling value score; S3: taking the space-time coupling value score as input, establishing a three-layer optimization architecture to realize globally optimal sample combination; S4: merging the selected samples of each cluster to form a final labeling set, performing quality evaluation on the final labeling set, and generating a standardized labeling output according to the quality evaluation result; and providing an accurate value basis for subsequent intelligent selection.

A system that obtains a plurality of sensor values associated with a deposition process performed in a process chamber to deposit film on a surface of a substrate. The system then applies a virtual model to the plurality of sensor values. The virtual model is trained based on historical sensor data and a plurality of physics based outputs used to process non-linear relationships between the historical sensor data from different sensors in the process chamber. The plurality of physics based outputs were generated using a transformation function and the historical sensor data. The transformation function comprises functions associated with at least one of chemical kinetics or Langmuir adsorption model. The system obtains an output of the virtual model, the output identifying predictive metrology data for the film.

To provide more test data during the manufacture of non-volatile memories and other integrated circuits, machine learning is used to generate virtual test values. Virtual test results are interpolated for one set of tests for devices on which the test is not performed based on correlations with other sets of tests. In one example, machine learning determines a correlation study between bad block values determined at die sort and photo-limited yield (PLY) values determined inline during processing. The correlation can be applied to interpolate virtual inline PLY data for all of the memory dies, allowing for more rapid feedback on the processing parameters for manufacturing the memory dies and making the manufacturing process more efficient and accurate. In another set of embodiments, the machine learning is used to extrapolate limited metrology (e.g., critical dimension) test data to all of the memory die through interpolated virtual metrology data values.

Described are systems and methods for predicting an overlay of a patterned wafer. Methods can include receiving a machine data set comprising parameters at a plurality of positions of a wafer, generating an integrated data set by aggregating the parameters and mapping the aggregated parameters to an equispatial grid, generating, using a trained machine learning (ML) model, a predicted overlay for the patterned wafer based at least in part on processing the integrated data set.

The present application provides a virtual measurement method and system with convolutional autoencoder and transfer learning. The modeling operation of the virtual measurement method with convolutional autoencoder and transfer learning includes classification of paired data and unpaired process data; a pre-training model is established using the unpaired process data, and then the paired data is input into the pre-training model to establish a virtual measurement model based on convolutional autoencoder, which includes a convolutional neural network model. In addition, the calculation operation includes one of the prediction stage and the transfer learning stage according to whether the actual measurement value is obtained, to calculate one of the first stage virtual measurement value and the second stage virtual measurement value. In this way, fast modeling can be achieved without collecting a large amount of paired data, and online relearning can be performed when the working conditions change to maintain the prediction accuracy.

The application provides a point cloud-based machining surface quality and roughness virtual measurement method and system, and belongs to the technical field of numerical control machining simulation and digital detection. The method comprises the following steps: obtaining a geometric model of the workpiece appearance after machining through machining process simulation; extracting three-dimensional point cloud data of the workpiece surface from the geometric model; performing spatial structure analysis on the point cloud data to determine the surface forming quality state; and calculating the surface roughness based on the forming quality state and the machining process parameters. The application adopts a three-dimensional discrete element model to perform high-precision material removal simulation, realizes efficient point cloud analysis through a KDTree structure, and establishes a roughness calculation model in combination with the tool geometry and motion parameters, thereby realizing accurate prediction of the surface quality before machining. The application solves the problem of disconnection between simulation and quality evaluation, can replace traditional post-detection, reduces the trial cutting cost, and provides data support for process optimization.

Embodiments of the present application provide a metrology model training method, a metrology method, a device, equipment, a medium and a product. The metrology method comprises: obtaining sensor data and metrology data of a wafer to be measured and a previous wafer adjacent to the wafer to be measured; performing first normalization processing on the sensor data of the wafer to be measured and the previous wafer adjacent to the wafer to be measured respectively to obtain text features corresponding to the wafer to be measured; performing second normalization processing on the metrology data of the wafer to be measured and the previous wafer adjacent to the wafer to be measured respectively to obtain image features corresponding to the wafer to be measured; inputting the text features and the image features corresponding to the wafer to be measured into a metrology model corresponding to a current machine, and taking an output wafer image as a metrology result; wherein the metrology model is a pre-trained multi-modal model, and is used to predict a wafer image of a wafer after the wafer passes through the machine, and the wafer image is wafer process data. The method can improve the accuracy of semiconductor virtual metrology.

A virtual metrology apparatus, a virtual metrology method, and a virtual metrology program that allow a highly accurate virtual metrology process to be performed is provided. A virtual metrology apparatus includes an acquisition unit configured to acquire a time series data group measured in association with processing of a target object in a predetermined processing unit of a manufacturing process, and a training unit configured to train a plurality of network sections by machine learning such that a result of consolidating output data produced by the plurality of network sections processing the acquired time series data group approaches inspection data of a resultant object obtained upon processing the target object in the predetermined processing unit of the manufacturing process.