Abstract: A ground simulation device and method for an on-orbit manipulation of a space manipulator is provided. The ground simulation device includes: a dual-arm robot, configured to simulate the space manipulator operating a target object; a suspension device, including a fixed post and passive rods, where the passive rods are movably connected with a top end of the fixed post, and the target object is suspended to the passive rods; and a simulation platform, configured to fix the dual-arm robot and the suspension device thereon. The ground simulation device provides the passive rods on the suspension device and suspends the target object to the passive rods, thus overcoming the gravity of the target object. In addition, the passive rods can drive the target object to move under an influence of an external force, achieving a similar suspension effect to that in space, and providing a desired, safe, and reliable implementation effect.

Abstract: Methods and systems for deep learning alignment for semiconductor applications are provided. One method includes transforming design information for an alignment target on a specimen to a predicted image of the alignment target by inputting the design information into a deep learning model and aligning the predicted image to an image of the alignment target on the specimen generated by an imaging subsystem. The method also includes determining an offset between the predicted image and the image generated by the imaging subsystem based on results of the aligning and storing the determined offset as an align-to-design offset for use in a process performed on the specimen with the imaging subsystem.

Abstract: A method for generating binding peptides presented by any given Major Histocompatibility Complex (MHC) protein is presented. The method includes, given a peptide and an MHC protein pair, enabling a Reinforcement Learning (RL) agent to interact with and exploit a peptide mutation environment by repeatedly mutating the peptide and observing an observation score of the peptide, learning to form a mutation policy, via a mutation policy network, to iteratively mutate amino acids of the peptide to obtain desired presentation scores, and generating, based on the desired presentation scores, qualified peptides and binding motifs of MHC Class I proteins.

Abstract: A method for generating binding peptides presented by any given Major Histocompatibility Complex (MHC) protein is presented. The method includes, given a peptide and an MHC protein pair, enabling a Reinforcement Learning (RL) agent to interact with and exploit a peptide mutation environment by repeatedly mutating the peptide and observing an observation score of the peptide, learning to form a mutation policy, via a mutation policy network, to iteratively mutate amino acids of the peptide to obtain desired presentation scores, and generating, based on the desired presentation scores, qualified peptides and binding motifs of MHC Class I proteins.

Abstract: A system for binding peptide search for immunotherapy is presented. The system includes employing a deep neural network to predict a peptide presentation given Major Histocompatibility Complex allele sequences and peptide sequences, training a Variational Autoencoder (VAE) to reconstruct peptides by converting the peptide sequences into continuous embedding vectors, running a Monte Carlo Tree Search to generate a first set of positive peptide vaccine candidates, running a Bayesian Optimization search with the trained VAE and a Backpropagation search with the trained VAE to generate a second set of positive peptide vaccine candidates, using a sampling from a Position Weight Matrix (sPWM) to generate a third set of positive peptide vaccine candidates, screening and merging the first, second, and third sets of positive peptide vaccine candidates, and outputting qualified peptides for immunotherapy from the screened and merged sets of positive peptide vaccine candidates.

Abstract: A system for binding peptide search for immunotherapy is presented. The system includes employing a deep neural network to predict a peptide presentation given Major Histocompatibility Complex allele sequences and peptide sequences, training a Variational Autoencoder (VAE) to reconstruct peptides by converting the peptide sequences into continuous embedding vectors, running a Monte Carlo Tree Search to generate a first set of positive peptide vaccine candidates, running a Bayesian Optimization search with the trained VAE and a Backpropagation search with the trained VAE to generate a second set of positive peptide vaccine candidates, using a sampling from a Position Weight Matrix (sPWM) to generate a third set of positive peptide vaccine candidates, screening and merging the first, second, and third sets of positive peptide vaccine candidates, and outputting qualified peptides for immunotherapy from the screened and merged sets of positive peptide vaccine candidates.

Abstract: A method for generating binding peptides presented by any given Major Histocompatibility Complex (MHC) protein is presented. The method includes, given a peptide and an MHC protein pair, enabling a Reinforcement Learning (RL) agent to interact with and exploit a peptide mutation environment by repeatedly mutating the peptide and observing an observation score of the peptide, learning to form a mutation policy, via a mutation policy network, to iteratively mutate amino acids of the peptide to obtain desired presentation scores, and generating, based on the desired presentation scores, qualified peptides and binding motifs of MHC Class I proteins.

Abstract: A system for binding peptide search for immunotherapy is presented. The system includes employing a deep neural network to predict a peptide presentation given Major Histocompatibility Complex allele sequences and peptide sequences, training a Variational Autoencoder (VAE) to reconstruct peptides by converting the peptide sequences into continuous embedding vectors, running a Monte Carlo Tree Search to generate a first set of positive peptide vaccine candidates, running a Bayesian Optimization search with the trained VAE and a Backpropagation search with the trained VAE to generate a second set of positive peptide vaccine candidates, using a sampling from a Position Weight Matrix (sPWM) to generate a third set of positive peptide vaccine candidates, screening and merging the first, second, and third sets of positive peptide vaccine candidates, and outputting qualified peptides for immunotherapy from the screened and merged sets of positive peptide vaccine candidates.

Abstract: Methods and structures of a three-dimensional memory device are disclosed. In an example, the method for forming a memory device includes the following operations. First, a plurality of first semiconductor channels can be formed over a first wafer with a peripheral device and a plurality of first via structures neighboring the plurality of first semiconductor channels. The plurality of first semiconductor channels can extend along a direction perpendicular to a surface of the first wafer. Further, a plurality of second semiconductor channels can be formed over a second wafer with a plurality of second via structures neighboring the plurality of second semiconductor channels. The plurality of second semiconductor channels can extend along a direction perpendicular to a surface of the second wafer and a peripheral via structure.

Abstract: A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy is presented. The method includes extracting peptides to identify a virus or tumor cells, collecting a library of TCRs from target patients, predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients, developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores, defining reward functions based on a reconstruction-based score and a density estimation-based score, randomly sampling batches of TCRs and following a policy network to mutate the TCRs, outputting mutated TCRs, and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.

Abstract: A system and a method of enhancing reliability of fused deposition modelling (FDM) process are disclosed. The system instructs a three-dimensional (3D) printer to employ a 3D printer nozzle for dispensing material for forming a 3D print object. The system receives images from a nozzle camera. The nozzle camera captures images of the 3D printer nozzle dispensing the material. The system detects printing failures from the images of the 3D printer nozzle. The system creates bounding boxes around the printing failures. The system classifies the printing failures based on type of errors. The system adjusts printing parameters or terminates the printing process based on the printing failures classified. The system further includes a bracket for positioning the nozzle camera for capturing images of the 3D printer nozzle.

Abstract: A method for generating binding peptides presented by any given Major Histocompatibility Complex (MHC) protein is presented. The method includes, given a peptide and an MHC protein pair, enabling a Reinforcement Learning (RL) agent to interact with and exploit a peptide mutation environment by repeatedly mutating the peptide and observing an observation score of the peptide, learning to form a mutation policy, via a mutation policy network, to iteratively mutate amino acids of the peptide to obtain desired presentation scores, and generating, based on the desired presentation scores, qualified peptides and binding motifs of MHC Class I proteins.

Abstract: A system for binding peptide search for immunotherapy is presented. The system includes employing a deep neural network to predict a peptide presentation given Major Histocompatibility Complex allele sequences and peptide sequences, training a Variational Autoencoder (VAE) to reconstruct peptides by converting the peptide sequences into continuous embedding vectors, running a Monte Carlo Tree Search to generate a first set of positive peptide vaccine candidates, running a Bayesian Optimization search with the trained VAE and a Backpropagation search with the trained VAE to generate a second set of positive peptide vaccine candidates, using a sampling from a Position Weight Matrix (sPWM) to generate a third set of positive peptide vaccine candidates, screening and merging the first, second, and third sets of positive peptide vaccine candidates, and outputting qualified peptides for immunotherapy from the screened and merged sets of positive peptide vaccine candidates.

Abstract: Embodiments of through array contact structures of a 3D memory device and fabricating method thereof are disclosed. The method comprises: forming a recess region in a substrate including multiple protruding islands; forming a gate dielectric layer to cover top surfaces and sidewalls of the multiple protruding islands and a top surface of the recess region of the substrate; forming an underlying sacrificial layer on the gate dielectric layer to surround the sidewalls of the multiple protruding islands; forming an alternating dielectric stack including multiple alternatively stacked insulating layers and sacrificial layers on the underlying sacrificial layer and the multiple protruding islands; forming multiple channel holes penetrating the alternating dielectric stack, each channel hole is located corresponding to one of the multiple protruding islands; and forming a memory layer in each channel hole, wherein a channel layer of the memory layer is electrically connected with a corresponding protruding island.

Abstract: Methods and structures of a three-dimensional memory device are disclosed. In an example, the disclosed memory device comprises multiple staircase structures stacked over a substrate. The multiple staircase structures are positioned in a dielectric fill structure over the substrate. Each staircase structure comprises multiple gate electrodes separated by multiple insulating layers. The memory device further comprises a semiconductor channel extending from through the multiple staircase structures into the substrate. A first portion of peripheral via structures extends through the dielectric fill structure and is connected to the gate electrodes of each staircase structure. A second portion of peripheral via structures extend through the dielectric fill structure and is connected to a peripheral device over the substrate and neighboring staircase structures.

Abstract: Methods and structures of a three-dimensional memory device are disclosed. In an example, the disclosed memory device comprises multiple staircase structures stacked over a substrate. The multiple staircase structures are positioned in a dielectric fill structure over the substrate. Each staircase structure comprises multiple gate electrodes separated by multiple insulating layers. The memory device further comprises a semiconductor channel extending from through the multiple staircase structures into the substrate. A first portion of peripheral via structures extends through the dielectric fill structure and is connected to the gate electrodes of each staircase structure. A second portion of peripheral via structures extend through the dielectric fill structure and is connected to a peripheral device over the substrate and neighboring staircase structures.

Abstract: Methods and structures of a three-dimensional memory device are disclosed. In an example, the method for forming a memory device includes the following operations. First, a plurality of first semiconductor channels can be formed over a first wafer with a peripheral device and a plurality of first via structures neighboring the plurality of first semiconductor channels. The plurality of first semiconductor channels can extend along a direction perpendicular to a surface of the first wafer. Further, a plurality of second semiconductor channels can be formed over a second wafer with a plurality of second via structures neighboring the plurality of second semiconductor channels. The plurality of second semiconductor channels can extend along a direction perpendicular to a surface of the second wafer and a peripheral via structure.

Abstract: Embodiments of through array contact structures of a 3D memory device and fabricating method thereof are disclosed. The method comprises: forming a recess region in a substrate including multiple protruding islands; forming a gate dielectric layer to cover top surfaces and sidewalls of the multiple protruding islands and a top surface of the recess region of the substrate; forming an underlying sacrificial layer on the gate dielectric layer to surround the sidewalls of the multiple protruding islands; forming an alternating dielectric stack including multiple alternatively stacked insulating layers and sacrificial layers on the underlying sacrificial layer and the multiple protruding islands; forming multiple channel holes penetrating the alternating dielectric stack, each channel hole is located corresponding to one of the multiple protruding islands; and forming a memory layer in each channel hole, wherein a channel layer of the memory layer is electrically connected with a corresponding protruding island.

Abstract: Methods and structures of a three-dimensional memory device are disclosed. In an example, the method for forming a memory device includes the following operations. First, a plurality of first semiconductor channels can be formed over a first wafer with a peripheral device and a plurality of first via structures neighboring the plurality of first semiconductor channels. The plurality of first semiconductor channels can extend along a direction perpendicular to a surface of the first wafer. Further, a plurality of second semiconductor channels can be formed over a second wafer with a plurality of second via structures neighboring the plurality of second semiconductor channels. The plurality of second semiconductor channels can extend along a direction perpendicular to a surface of the second wafer and a peripheral via structure.

Abstract: Embodiments of through array contact structures of a 3D memory device and fabricating method thereof are disclosed. The method comprises: forming a recess region in a substrate including multiple protruding islands; forming a gate dielectric layer to cover top surfaces and sidewalls of the multiple protruding islands and a top surface of the recess region of the substrate; forming an underlying sacrificial layer on the gate dielectric layer to surround the sidewalls of the multiple protruding islands; forming an alternating dielectric stack including multiple alternatively stacked insulating layers and sacrificial layers on the underlying sacrificial layer and the multiple protruding islands; forming multiple channel holes penetrating the alternating dielectric stack, each channel hole is located corresponding to one of the multiple protruding islands; and forming a memory layer in each channel hole, wherein a channel layer of the memory layer is electrically connected with a corresponding protruding island.