Abstract: A semiconductor device and a method of forming the same is disclosed. The semiconductor device includes a substrate, a first well region disposed within the substrate, a second well region disposed adjacent to the first well region and within the substrate, and an array of well regions disposed within the first well region. The first well region includes a first type of dopants, the second well region includes a second type of dopants that is different from the first type of dopants, and the array of well regions include the second type of dopants. The semiconductor device further includes a metal silicide layer disposed on the array of well regions and within the substrate, a metal silicide nitride layer disposed on the metal silicide layer and within the substrate, and a contact structure disposed on the metal silicide nitride layer.

Abstract: A photo resist layer is used to protect a dielectric layer and conductive elements embedded in the dielectric layer when patterning an etch stop layer underlying the dielectric layer. The photo resist layer may further be used to etch another dielectric layer underlying the etch stop layer, where etching the next dielectric layer exposes a contact, such as a gate contact. The bottom layer can be used to protect the conductive elements embedded in the dielectric layer from a wet etchant used to etch the etch stop layer.

Abstract: Embodiments of the technology described herein, make unknown material-maps in a Physically Based Rendering (PBR) asset usable through an identification process that relies, at least in part, on image analysis. In addition, when a desired material-map type is completely missing from a PBR asset the technology described herein may generate a suitable synthetic material map for use in rendering. In one aspect, the correct map type is assigned using a machine classifier, such as a convolutional neural network, which analyzes image content of the unknown material map and produce a classification. The technology described herein also correlates material maps into material definitions using a combination of the material-map type and similarity analysis. The technology described herein may generate synthetic maps to be used in place of the missing material maps. The synthetic maps may be generated using a Generative Adversarial Network (GAN).