Abstract: Disclosed is a transient-state THz spectrometer applied to cells and biological macromolecules, including a femtosecond laser amplifier. A femtosecond laser output by the femtosecond laser amplifier is divided into two beams of pump light and probe light after passing through a beam splitter of which a transmission-reflection ratio is 7:3, the pump light is focused to irradiate a gap between electrodes of a nonlinear photoconductive antenna and emit a terahertz wave after successively passing through a half wave plate, a silver-plated reflector and a first lens, the terahertz wave forms a terahertz wave collineation after successively passing through a second lens, a slab waveguide, a third lens and an ITO film, the terahertz wave collineation and the probe light form a probe light collineation of wavefront tilt which is perpendicularly incident on a ZnTe crystal and detected and recorded by using a CCD camera.

Abstract: A method for calibrating a process model and training an inverse process model of a patterning process. The training method includes obtaining a first patterning device pattern from simulation of an inverse lithographic process that predicts a patterning device pattern based on a wafer target layout, receiving wafer data corresponding to a wafer exposed using the first patterning device pattern, and training an inverse process model configured to predict a second patterning device pattern using the wafer data related to the exposed wafer and the first patterning device pattern.

Abstract: A method is described. The method includes performing the following with a storage end transaction agent within a storage sled of a rack mounted computing system: receiving a request to perform storage operations with one or more storage devices of the storage sled, the request specifying an all-or-nothing semantic for the storage operations; recognizing that all of the storage operations have successfully completed; after all of the storage operations have successfully completed, reporting to a CPU side transaction agent that sent the request that all of the storage operations have successfully completed.

Abstract: A method for calibrating a process model and training an inverse process model of a patterning process. The training method includes obtaining a first patterning device pattern from simulation of an inverse lithographic process that predicts a patterning device pattern based on a wafer target layout, receiving wafer data corresponding to a wafer exposed using the first patterning device pattern, and training an inverse process model configured to predict a second patterning device pattern using the wafer data related to the exposed wafer and the first patterning device pattern.

Abstract: An apparatus is described. The apparatus includes a memory controller to interface with a multi-level memory, where, an upper level of the multi-level memory is to act as a cache for a lower level of the multi-level memory. The memory controller has circuitry to determine: i) an original address of a slot in the upper level of memory from an address of a memory request in a direct mapped fashion; ii) a miss in the cache for the request because the slot is pinned with data from another address that competes with the address; iii) a partner slot of the slot in the cache in response to the miss; iv) whether there is a hit or miss in the partner slot in the cache for the request.

Abstract: A method for recovering C2 components in a methane-containing industrial gas includes the steps of (1) cooling a compressed methane-containing industrial gas and performing gas-liquid separation; (2) absorbing C2 components in the gas phase by using an absorbent to obtain an absorption rich liquid; (3) returning the absorption rich liquid to the compression in step (1) or mixing the absorption rich liquid with the liquid phase obtained in step (1) to obtain a mixed liquid, and depressurizing the mixed liquid or the absorption rich liquid; (4) performing methane desorption on the depressurized stream to obtain a rich absorbent, or performing second gas-liquid separation on the depressurized stream, followed by methane desorption on the second liquid phase to obtain a rich absorbent; and (5) desorbing and separating the rich absorbent to obtain a lean absorbent and an enriched gas, and recycling and reusing the lean absorbent.

Abstract: Methods of generating a characteristic pattern for a patterning process and training a machine learning model. A method of training a machine learning model configured to generate a characteristic pattern for a mask pattern includes obtaining (i) a reference characteristic pattern that meets a satisfactory threshold related to manufacturing of the mask pattern, and (ii) a continuous transmission mask (CTM) for use in generating the mask pattern; and training, based on the reference characteristic pattern and the CTM, the machine learning model such that a first metric between the characteristic pattern and the CTM, and a second metric between the characteristic pattern and the reference characteristic pattern is reduced.

Abstract: A method for determining a model to predict overlay data associated with a current substrate being patterned. The method involves obtaining (i) a first data set associated with one or more prior layers and/or current layer of the current substrate, (ii) a second data set including overlay metrology data associated with one or more prior substrates, and (iii) de-corrected measured overlay data associated with the current layer of the current substrate; and determining, based on (i) the first data set, (ii) the second data set, and (iii) the de-corrected measured overlay data, values of a set of model parameters associated with the model such that the model predicts overlay data for the current substrate, wherein the values are determined such that a cost function is minimized, the cost function comprising a difference between the predicted data and the de-corrected measured overlay data.

Abstract: Methods of identifying a hot spot from a design layout or of predicting whether a pattern in a design layout is defective, using a machine learning model. An example method disclosed herein includes obtaining sets of one or more characteristics of performance of hot spots, respectively, under a plurality of process conditions, respectively, in a device manufacturing process; determining, for each of the process conditions, for each of the hot spots, based on the one or more characteristics under that process condition, whether that hot spot is defective; obtaining a characteristic of each of the process conditions; obtaining a characteristic of each of the hot spots; and training a machine learning model using a training set including the characteristic of one of the process conditions, the characteristic of one of the hot spots, and whether that hot spot is defective under that process condition.

Abstract: Methods of identifying a hot spot from a design layout or of predicting whether a pattern in a design layout is defective, using a machine learning model. An example method disclosed herein includes obtaining sets of one or more characteristics of performance of hot spots, respectively, under a plurality of process conditions, respectively, in a device manufacturing process; determining, for each of the process conditions, for each of the hot spots, based on the one or more characteristics under that process condition, whether that hot spot is defective; obtaining a characteristic of each of the process conditions; obtaining a characteristic of each of the hot spots; and training a machine learning model using a training set including the characteristic of one of the process conditions, the characteristic of one of the hot spots, and whether that hot spot is defective under that process condition.

Abstract: Technologies for fast distributed storage recovery include a distributed storage system that includes multiple controller nodes and multiple target nodes. Each controller node is coupled to a corresponding target node via a storage fabric. Each target node stores replica data. The system identifies a failed node and a corresponding node that was coupled to the failed node. If the failed node is a controller node, the corresponding node is a target node. If the failed node is a target node, the corresponding node is a controller node. The system instantiates a replacement node, adds the replacement node to the system, and couples the replacement node to the corresponding node. The system may direct a backup target node to copy replica data to the replacement target node via the storage fabric. Other embodiments are described and claimed.

Abstract: In one embodiment, an edge compute node comprises processing circuitry to: receive an incoming video stream captured by a camera, wherein the incoming video stream comprises a plurality of video segments; store the plurality of video segments in a receive buffer in a memory; perform a visual computing task on a first video segment in the receive buffer; detect a resource overload on the edge compute node; receive load information corresponding to a plurality of peer compute nodes; select a peer compute node to perform the visual computing task on a second video segment in the receive buffer; replicate the second video segment from the edge compute node to the peer compute node; and receive a compute result from the peer compute node, wherein the compute result is based on the peer compute node performing the visual computing task on the second video segment.

Abstract: In one embodiment, an apparatus comprises processing circuitry to: receive, via a network interface, a video stream comprising a plurality of video frames; identify a plurality of dependencies among the plurality of video frames; identify, based on the plurality of dependencies, a first subset of video frames to be transmitted using a first transmission method and a second subset of video frames to be transmitted using a second transmission method, wherein the first subset of video frames and the second subset of video frames are identified from the plurality of video frames, and wherein the first transmission method provides a higher quality of service than the second transmission method; transmit, via the network interface, the first subset of video frames to a corresponding destination using the first transmission method; and transmit, via the network interface, the second subset of video frames to the corresponding destination using the second transmission method.

Abstract: In one embodiment, a system comprises a host processor and a storage system. The storage system comprises one or more storage devices, and each storage device comprises a non-volatile memory and a compute offload controller. The non-volatile memory stores data, and the compute offload controller performs compute tasks on the data based on compute offload commands from the host processor.

Abstract: A method to determine a curvilinear pattern of a patterning device that includes obtaining (i) an initial image of the patterning device corresponding to a target pattern to be printed on a substrate subjected to a patterning process, and (ii) a process model configured to predict a pattern on the substrate from the initial image, generating, by a hardware computer system, an enhanced image from the initial image, generating, by the hardware computer system, a level set image using the enhanced image, and iteratively determining, by the hardware computer system, a curvilinear pattern for the patterning device based on the level set image, the process model, and a cost function, where the cost function (e.g., EPE) determines a difference between a predicted pattern and the target pattern, where the difference is iteratively reduced.

Abstract: A wearable device can provide an audio module that is operable to provide audio output from a distance away from the ears of the user. For example, the wearable device can be worn on clothing of the user and direct audio waves to the ears of the user. Such audio waves can be focused by a parametric array of speakers that limit audibility by others. Thus, the privacy of the audio directed to the user can be maintained without requiring the user to wear audio headsets on, over, or in the ears of the user. The wearable device can further include microphones and/or connections to other devices that facilitate calibration of the audio module of the wearable device. The wearable device can further include user sensors that are configured to detect, measure, and/or track one or more properties of the user.

Abstract: Methods for training a process model and determining ranking of simulated patterns (e.g., corresponding to hot spots). A method involves obtaining a training data set including: (i) a simulated pattern associated with a mask pattern to be printed on a substrate, (ii) inspection data of a printed pattern imaged on the substrate using the mask pattern, and (iii) measured values of a parameter of the patterning process applied during imaging of the mask pattern on the substrate; and training a machine learning model for the patterning process based on the training data set to predict a difference in a characteristic of the simulated pattern and the printed pattern. The trained machine learning model can be used for determining a ranking of hot spots. In another method a model is trained based on measurement data to predict ranking of the hot spots.

Abstract: A method for determining a correction relating to a performance metric of a semiconductor manufacturing process, the method including: obtaining a set of pre-process metrology data; processing the set of pre-process metrology data by decomposing the pre-process metrology data into one or more components which: a) correlate to the performance metric; or b) are at least partially correctable by a control process which is part of the semiconductor manufacturing process; and applying a trained model to the processed set of pre-process metrology data to determine the correction for the semiconductor manufacturing process.

Abstract: A method to determine a curvilinear pattern of a patterning device that includes obtaining (i) an initial image of the patterning device corresponding to a target pattern to be printed on a substrate subjected to a patterning process, and (ii) a process model configured to predict a pattern on the substrate from the initial image, generating, by a hardware computer system, an enhanced image from the initial image, generating, by the hardware computer system, a level set image using the enhanced image, and iteratively determining, by the hardware computer system, a curvilinear pattern for the patterning device based on the level set image, the process model, and a cost function, where the cost function (e.g., EPE) determines a difference between a predicted pattern and the target pattern, where the difference is iteratively reduced.

Abstract: In one embodiment, an edge compute node comprises processing circuitry to: receive an incoming video stream captured by a camera, wherein the incoming video stream comprises a plurality of video segments; store the plurality of video segments in a receive buffer in a memory; perform a visual computing task on a first video segment in the receive buffer; detect a resource overload on the edge compute node; receive load information corresponding to a plurality of peer compute nodes; select a peer compute node to perform the visual computing task on a second video segment in the receive buffer; replicate the second video segment from the edge compute node to the peer compute node; and receive a compute result from the peer compute node, wherein the compute result is based on the peer compute node performing the visual computing task on the second video segment.