Abstract: A method includes detecting, by a first data storage device, an error when reading data from the first data storage device. The method further includes correcting a portion of the error, by a controller of a host system, using host-level outer codes; and communicating, by the controller, error correction information to the first data storage device in response to correcting the portion of the error using the host-level outer codes.

Abstract: A method includes receiving, by a data storage device, a read command. The method further includes reading a first set of outer code stored to a magnetic recording medium of the data storage device and storing the first set of outer code to memory. The method further includes receiving a write command to write data to the magnetic recording medium and writing a second set of outer code to the magnetic recording medium in connection with the write command.

Abstract: One embodiment of the present invention sets forth a technique for adding dimensions to a target drawing. The technique includes generating a first set of node embeddings for a first set of nodes included in a target graph that represents the target drawing. The technique also includes receiving a second set of node embeddings for a second set of nodes included in a source graph that represents a source drawing, where one or more nodes included in the second set of nodes are associated with one or more source dimensions included in the source drawing. The technique further includes generating a set of mappings between the first and second sets of nodes based similarities between the first set of node embeddings and the second set of node embeddings, and automatically placing the one or more source dimensions within the target drawing based on the set of mappings.

Abstract: In various embodiments, a training application generates a trained encoder that automatically generates shape embeddings having a first size and representing three-dimensional (3D) geometry shapes. First, the training application generates a different view activation for each of multiple views associated with a first 3D geometry based on a first convolutional neural network (CNN) block. The training application then aggregates the view activations to generate a tiled activation. Subsequently, the training application generates a first shape embedding having the first size based on the tiled activation and a second CNN block. The training application then generates multiple re-constructed views based on the first shape embedding. The training application performs training operation(s) on at least one of the first CNN block and the second CNN block based on the views and the re-constructed views to generate the trained encoder.

Abstract: An inductor includes a planar laminated magnetic core and a conductive winding. The planar magnetic core includes an alternating sequence of a magnetic layer and a non-magnetic layer. The non-magnetic layer includes an insulating layer that is disposed between first and second interface layers. The conductive winding turns around in a generally spiral manner on the outside of the planar laminated magnetic core. The inductor can be integrated into a multilevel wiring network in a semiconductor integrated circuit to form a microelectronic device, such as a transformer, a power converter, or a microprocessor.

Abstract: A method for manufacturing a ferromagnetic-dielectric composite material comprises: (a) placing patterned ferromagnetic layer regions, in a patterning substrate assembly that includes a patterning substrate and a first dielectric layer, in physical contact with a second dielectric layer, the second dielectric layer in a receiving substrate assembly that includes a receiving substrate, (b) forming a bond between the patterned ferromagnetic layer regions and the second dielectric layer; (c) releasing the patterning substrate from the patterning substrate assembly to transfer the patterned ferromagnetic layer regions and the first dielectric layer from the patterning substrate assembly to the receiving substrate assembly; and (d) releasing the receiving substrate from the receiving substrate assembly to form the ferromagnetic-dielectric composite material.

Abstract: A method for manufacturing a vertically-laminated ferromagnetic core includes (a) depositing a conductive seed layer on or over a first side of a substrate; (b) depositing a masking layer on or over a second side of the substrate, the first and second sides on opposite sides of the substrate; (c) forming a pattern in the masking layer; (d) dry etching the substrate, based on the pattern in the masking layer, from the second side to the first side to expose portions of the conductive seed layer; and (e) depositing a ferromagnetic material onto the exposed portions of the conductive seed layer to form vertically-oriented ferromagnetic layers.

Abstract: A method for manufacturing a vertically-laminated ferromagnetic core includes (a) depositing a conductive seed layer on or over a first side of a substrate; (b) depositing a masking layer on or over a second side of the substrate, the first and second sides on opposite sides of the substrate; (c) forming a pattern in the masking layer; (d) dry etching the substrate, based on the pattern in the masking layer, from the second side to the first side to expose portions of the conductive seed layer; and (e) depositing a ferromagnetic material onto the exposed portions of the conductive seed layer to form vertically-oriented ferromagnetic layers.

Abstract: A ferromagnetic-polymer composite material comprises a polymer and a plurality of ferromagnetic film platelets disposed in the polymer. Each ferromagnetic film platelet comprises first and second insulator layers and a ferromagnetic layer disposed between the first and second insulator layers. The ferromagnetic layer can be magnetically anisotropic in which a hard axis of magnetization is aligned parallel to a plane that passes through and parallel to an interface between the first insulator layer and the ferromagnetic layer. The easy and/or hard axes of magnetization in the ferromagnetic film platelets can be aligned. An inductor can have a core formed of the ferromagnetic-polymer composite material.

Abstract: One embodiment of the present invention sets forth a technique for adding dimensions to a target drawing. The technique includes generating a first set of node embeddings for a first set of nodes included in a target graph that represents the target drawing. The technique also includes receiving a second set of node embeddings for a second set of nodes included in a source graph that represents a source drawing, where one or more nodes included in the second set of nodes are associated with one or more source dimensions included in the source drawing. The technique further includes generating a set of mappings between the first and second sets of nodes based similarities between the first set of node embeddings and the second set of node embeddings, and automatically placing the one or more source dimensions within the target drawing based on the set of mappings.

Abstract: In various embodiments, a parameter domain graph application generates UV-net representations of 3D CAD objects for machine learning models. In operation, the parameter domain graph application generates a graph based on a B-rep of a 3D CAD object. The parameter domain graph application discretizes a parameter domain of a parametric surface associated with the B-rep into a 2D grid. The parameter domain graph application computes at least one feature at a grid point included in the 2D grid based on the parametric surface to generate a 2D UV-grid. Based on the graph and the 2D UV-grid, the parameter domain graph application generates a UV-net representation of the 3D CAD object. Advantageously, generating UV-net representations of 3D CAD objects that are represented using B-reps enables the 3D CAD objects to be processed efficiently using neural networks.

Abstract: In various embodiments, a training application trains machine learning models to perform tasks associated with 3D CAD objects that are represented using B-reps. In operation, the training application computes a preliminary result via a machine learning model based on a representation of a 3D CAD object that includes a graph and multiple 2D UV-grids. Based on the preliminary result, the training application performs one or more operations to determine that the machine learning model has not been trained to perform a first task. The training application updates at least one parameter of a graph neural network included in the machine learning model based on the preliminary result to generate a modified machine learning model. The training application performs one or more operations to determine that the modified machine learning model has been trained to perform the first task.

Abstract: In various embodiments, an inference application performs tasks associated with 3D CAD objects that are represented using B-reps. A UV-net representation of a 3D CAD object that is represented using a B-rep includes a set of 2D UV-grids and a graph. In operation, the inference application maps the set of 2D UV-grids to a set of node feature vectors via a trained neural network. Based on the node feature vectors and the graph, the inference application computes a final result via a trained graph neural network. Advantageously, the UV-net representation of the 3D CAD object enabled the trained neural network and the trained graph neural network to efficiently process the 3D CAD object.

Abstract: In various embodiments, a training application generates a trained encoder that automatically generates shape embeddings having a first size and representing three-dimensional (3D) geometry shapes. First, the training application generates a different view activation for each of multiple views associated with a first 3D geometry based on a first convolutional neural network (CNN) block. The training application then aggregates the view activations to generate a tiled activation. Subsequently, the training application generates a first shape embedding having the first size based on the tiled activation and a second CNN block. The training application then generates multiple re-constructed views based on the first shape embedding. The training application performs training operation(s) on at least one of the first CNN block and the second CNN block based on the views and the re-constructed views to generate the trained encoder.

Abstract: A planar magnetic core includes multiple ferromagnetic layers including multiple hard ferromagnetic bias layers and multiple soft ferromagnetic layers. Each ferromagnetic layer comprises a soft ferromagnetic layer or a hard ferromagnetic bias layer. Each hard ferromagnetic bias layer is a neighboring ferromagnetic layer of at least one soft ferromagnetic layer. The planar magnetic core also includes a plurality of insulating layers, each insulating layer disposed between adjacent ferromagnetic layers. Each ferromagnetic layer has an easy axis of magnetization parallel to a principal plane of the planar magnetic core, where the easy axes of magnetization are aligned. Each hard ferromagnetic bias layer is magnetized to create an in-plane bias magnetic flux through the hard ferromagnetic bias layer in a first direction that is parallel to the easy axis of magnetization and forms a closed path through a neighboring soft ferromagnetic layer in a second direction parallel to the first direction.

Abstract: A method of fabricating an inductor includes (a) forming a ferromagnetic core on a semiconductor substrate, the ferromagnetic core lying in a core plane and (b) fabricating an inductor coil that winds around the ferromagnetic core, the inductor coil configured to generate an inductor magnetic field that passes through the ferromagnetic core in a first direction parallel to the core plane. While forming the ferromagnetic core, the method further includes (1) generating a bias magnetic field that passes through the ferromagnetic core in a second direction that is orthogonal to the first direction, and (2) inducing a magnetic anisotropy in the ferromagnetic core with the bias magnetic field.

Abstract: A ferromagnetic actuator is disposed between first and second semiconductor devices that include first and second inductors. Each inductor is disposed on top of a multilevel wiring structure. Current flows through the first inductor to generate a first magnetic field that attracts the ferromagnetic actuator towards the first inductor causing the ferromagnetic actuator to transition from a first state to a second state. In the second state, a portion of the ferromagnetic actuator is disposed closer to the first inductor than it is in the first state. Current flows through the second inductor to generate a second magnetic field that attracts the ferromagnetic actuator towards the second inductor causing the ferromagnetic actuator to transition from the first or second state to a third state. In the third state, a portion of the ferromagnetic actuator is disposed closer to the first inductor than it is in the first state.

Abstract: A switched inductor DC-DC power converter chiplet includes a CMOS power switch, an LC filter, regulation circuitry, feedback control circuitry, and interface control circuitry integrated on a common substrate. The inductor for the LC filter can be formed on the same surface or on opposing surfaces of the substrate as the electrical terminations for the substrate. Another embodiment includes a switched inductor DC-DC power converter chiplet having a first powertrain phase and multiple second powertrain phases. When the load current is less than or equal to a threshold load current, the power conversion efficiency can be improved by only operating the first powertrain phase. When the load current is greater than the threshold load current, the power conversion efficiency can be improved by operating one or more second powertrain phases.

Abstract: A structure comprises a semiconductor integrated circuit, an inductor, and a magnetic flux closure layer. The inductor is integrated into a multilevel wiring network in the semiconductor integrated circuit. The inductor includes a planar laminated magnetic core and a conductive winding that turns around in a generally spiral manner on the outside of the planar laminated magnetic core. The planar laminated magnetic core includes an alternating sequence of a magnetic layer and a non-magnetic layer. The magnetic flux closure layer is disposed within about 100 ?m of a face of the planar laminated magnetic core, the face of the planar magnetic core parallel to a principal plane of the planar laminated magnetic core. A second magnetic flux closure layer can be disposed within about 100 ?m of an opposing face of the planar laminated magnetic core.

Abstract: An inductor includes a planar laminated magnetic core and a conductive winding. The planar magnetic core includes an alternating sequence of a magnetic layer and a non-magnetic layer. The non-magnetic layer includes an insulating layer that is disposed between first and second interface layers. The conductive winding turns around in a generally spiral manner on the outside of the planar laminated magnetic core. The inductor can be integrated into a multilevel wiring network in a semiconductor integrated circuit to form a microelectronic device, such as a transformer, a power converter, or a microprocessor.