Dilute magnetic contact for a spin GaN HEMT

Semiconductor CMOS nano-electronics is intensively seeking solutions for future digital applications. One of the most promising solutions to deliver a technological breakthrough is exploring electron spin in metals and semiconductors with applications from spin transistors to quantum sensors, and quantum computing. Spintronic applications rely on magnetic semiconductor materials with suitable properties. In particular, dilute magnetic semiconductors (DMS), such as Mn doped GaN, show the great promise of a high Curie temperature (220K–370K), exceeding room temperature, and a large concentration of holes. These are all the essential pre-requisites for operation of spin transistors in circuits. In this work, we dope an AlGaN/GaN heterostructure consisting of a GaN (2 nm) cap layer, an Al0.25Ga0.75N (25 nm) barrier, and a GaN (2 μm) substrate grown on a 6” Si wafer with Mn by sputtering deposition and thermal annealing to create a dilute magnetic semiconductor material following the process flow. While initial attempts resulted in the formation of a MnO surface layer, the SEM/XDS and XPS data suggest a diffusion of Mn into the GaN layer using thermal annealing at 900◦C for 7h with a concentration of 4.5% which is very close to the desired concentration of 5% needed for a DMS. The annealing temperature has to be below 1000◦ C since temperatures around 1000◦C result in significant damage to the 2DEG and diffusion of Al from the AlGaN layer

Constructing Regularity Feature Trees for Solid Models

Approximate geometric models, e.g. as created by reverse engineering, describe the approximate shape of an object, but do not record the underlying design intent. Automatically inferring geometric aspects of the design intent, represented by feature trees and geometric constraints, enhances the utility of such models for downstream tasks. One approach to design intent detection in such models is to decompose them into regularity features. Geometric regularities such as symmetries may then be sought in each regularity feature, and subsequently be combined into a global, consistent description of the model’s geometric design intent. This paper describes a systematic approach for finding such regularity features based on recovering broken symmetries in the model. The output is a tree of regularity features for subsequent use in regularity detection and selection. Experimental results are given to demonstrate the operation and efficiency of the algorithm