Abstract: A neural network comprising at least one layer containing a set of units having an input thereto and an output therefrom, the input being arranged to have data input thereto representing an n-dimensional grid comprising a plurality of cells; the set of units within the layer being arranged to output result data to a further layer the set of units within the layer being arranged to perform a convolution operation on the input data; and wherein the convolution operation is implemented using a feature centric voting scheme applied to the non-zero cells in the input to the layer.

Abstract: Multi-dimensional data can be mapped to a projection shape and converted for image analysis. In some examples, the multi-dimensional data may include data captured by a LIDAR system for use in conjunction with a perception system for an autonomous vehicle. Converting operations can include converting three-dimensional LIDAR data to multi-channel two-dimensional data. Data points of the multi-dimensional data can be mapped to a projection shape, such as a sphere. Characteristics of the projection shape may include a shape, a field of view, a resolution, and a projection type. After data is mapped to the projection shape, the projection shape can be converted to a multi-channel, two-dimensional image. Image segmentation and classification may be performed on the two-dimensional data. Further, segmentation information may be used to segment the three-dimensional LIDAR data, while a rendering plane may be positioned relative to the segmented data to perform classification on a per-object basis.

Abstract: A vehicle can include various sensors to detect objects in an environment. Sensor data can be captured by a perception system in a vehicle and represented in a voxel space. Operations may include analyzing the data from a top-down perspective. From this perspective, techniques can associate and generate masks that represent objects in the voxel space. Through manipulation of the regions of the masks, the sensor data and/or voxels associated with the masks can be clustered or otherwise grouped to segment data associated with the objects.

Abstract: Systems, methods, and apparatuses described herein are directed to performing segmentation on voxels representing three-dimensional data to identify static and dynamic objects. LIDAR data may be captured by a perception system for an autonomous vehicle and represented in a voxel space. Operations may include determining a drivable surface by parsing individual voxels to determine an orientation of a surface normal of a planar approximation of the voxelized data relative to a reference direction. Clustering techniques can be used to grow a ground plane including a plurality of locally flat voxels. Ground plane data can be set aside from the voxel space, and the remaining voxels can be clustered to determine objects. Voxel data can be analyzed over time to determine dynamic objects. Segmentation information associated with ground voxels, static object, and dynamic objects can be provided to a tracker and/or planner in conjunction with operating the autonomous vehicle.

Abstract: A method for forming a trench capacitor without an additional mask adder and the resulting device are provided. Embodiments include forming a buried implant layer over a substrate; forming an EPI layer over the buried implant layer; forming an oxide layer over the EPI layer; forming a nitride layer over the oxide layer; forming first and second trenches in the nitride layer, the oxide layer, the EPI layer, the buried implant layer and the substrate, the first trench being wider and deeper than the second trench; forming a dielectric layer in the trenches; forming a first polysilicon layer over the dielectric layer in the trenches; removing the first polysilicon layer and the dielectric layer above the EPI layer in the trenches and at a bottom of the first trench; and forming a second polysilicon layer filling the first trench and above the EPI layer in the second trench.

Abstract: A system may receive a sensor dataset representing an environment and use the dataset to create or update a map. In creating or updating the map, the system may determine an object classification of one or more detected objects and only selectively incorporate data into the map based at least in part on the classification. The map may be associated with the classification (or semantic) information of the objects, as well as weights based on the classification. Similarly, datasets with selected classes of data removed may be used for system localization. Further, the system may determine an object track of the objects. When updating the map, voxels in a voxel space may indicate an occupied voxel based on a threshold number of observances. The object track and clean map can then be used for controlling an autonomous vehicle.

Abstract: A method for forming a trench capacitor without an additional mask adder and the resulting device are provided. Embodiments include forming a buried implant layer over a substrate; forming an EPI layer over the buried implant layer; forming an oxide layer over the EPI layer; forming a nitride layer over the oxide layer; forming first and second trenches in the nitride layer, the oxide layer, the EPI layer, the buried implant layer and the substrate, the first trench being wider and deeper than the second trench; forming a dielectric layer in the trenches; forming a first polysilicon layer over the dielectric layer in the trenches; removing the first polysilicon layer and the dielectric layer above the EPI layer in the trenches and at a bottom of the first trench; and forming a second polysilicon layer filling the first trench and above the EPI layer in the second trench.

Abstract: A method and system for detecting objects within a three-dimensional (3D) environment, comprising obtaining a 3D point-cloud representation of the environment, the point-cloud comprising a set of point locations, and converting the point-cloud to a 3D feature grid, wherein converting the point-cloud to the 3D feature grid comprises discretizing the 3D space into cells according to a grid size; determining which cells contain points from the 3D point-cloud; and mapping any that do to a feature vector; and mapping any that do not to a zero feature vector.

Abstract: Device and method of forming a non-volatile memory (NVM) device are disclosed. The NVM device includes NVM cells disposed on a substrate in a device region. The NVM cell includes a floating gate (FG) with first and second FG sidewalls disposed on the substrate and an intergate dielectric layer disposed over the FG and substrate. Re-entrants are disposed at corners of the intergate dielectric which are filled by dielectric re-entrant spacers. An access gate (AG) with first and second AG sidewalls is disposed on the substrate adjacent to the FG such that the second AG sidewall is adjacent to a first FG sidewall and separated by the intergate dielectric layer and the re-entrant spacers prevent AG from filling the re-entrants. A first source/drain (S/D) region is disposed in the substrate adjacent to the first AG sidewall and a second S/D region is disposed in the substrate adjacent to the second FG sidewall.

Abstract: Device and method of forming a non-volatile memory (NVM) device are disclosed. The NVM device includes NVM cells disposed on a substrate in a device region. The NVM cell includes a floating gate (FG) with first and second FG sidewalls disposed on the substrate and an intergate dielectric layer disposed over the FG and substrate. Re-entrants are disposed at corners of the intergate dielectric which are filled by dielectric re-entrant spacers. An access gate (AG) with first and second AG sidewalls is disposed on the substrate adjacent to the FG such that the second AG sidewall is adjacent to a first FG sidewall and separated by the intergate dielectric layer and the re-entrant spacers prevent AG from filling the re-entrants. A first source/drain (S/D) region is disposed in the substrate adjacent to the first AG sidewall and a second S/D region is disposed in the substrate adjacent to the second FG sidewall.

Abstract: Systems, methods, and apparatuses described herein are directed to performing segmentation on voxels representing three-dimensional data to identify static and dynamic objects. LIDAR data may be captured by a perception system for an autonomous vehicle and represented in a voxel space. Operations may include determining a drivable surface by parsing individual voxels to determine an orientation of a surface normal of a planar approximation of the voxelized data relative to a reference direction. Clustering techniques can be used to grow a ground plane including a plurality of locally flat voxels. Ground plane data can be set aside from the voxel space, and the remaining voxels can be clustered to determine objects. Voxel data can be analyzed over time to determine dynamic objects. Segmentation information associated with ground voxels, static object, and dynamic objects can be provided to a tracker and/or planner in conjunction with operating the autonomous vehicle.

Abstract: A method for forming a trench capacitor without an additional mask adder and the resulting device are provided. Embodiments include forming a buried implant layer over a substrate; forming an EPI layer over the buried implant layer; forming an oxide layer over the EPI layer; forming a nitride layer over the oxide layer; forming first and second trenches in the nitride layer, the oxide layer, the EPI layer, the buried implant layer and the substrate, the first trench being wider and deeper than the second trench; forming a dielectric layer in the trenches; forming a first polysilicon layer over the dielectric layer in the trenches; removing the first polysilicon layer and the dielectric layer above the EPI layer in the trenches and at a bottom of the first trench; and forming a second polysilicon layer filling the first trench and above the EPI layer in the second trench.

Abstract: Methods for fabricating integrated circuits are provided. In one example, a method includes depositing an ILD layer overlying a SOI substrate including a device structure and an isolation structure. The device structure is disposed on a semiconductor layer of the SOI substrate and includes a metal silicide region and the isolation structure extends through the semiconductor layer to a buried insulator layer of the SOI substrate. A patterned mask is used for etching through the ILD layer and forming a device contact opening that exposes the metal silicide region and a substrate contact opening that exposes the isolation structure. A device contact is formed in the device contact opening. The isolation structure and the buried insulator layer are etched through to extend the substrate contact opening to a support substrate of the SOI substrate. A substrate contact is formed in the substrate contact opening.

Abstract: A method and system for detecting objects within a three-dimensional (3D) environment, comprising obtaining a 3D point-cloud representation of the environment, the point-cloud comprising a set of point locations, and converting the point-cloud to a 3D feature grid, wherein converting the point-cloud to the 3D feature grid comprises discretising the 3D space into cells according to a grid size; determining which cells contain points from the 3D point-cloud; and mapping any that do to a feature vector; and mapping any that do not to a zero feature vector.