Wolfspeed, Inc.

Matthew David Conrad

Robert Tyler Leonard

Edward Robert Van Brunt

Valeri Fedorovich Tsvetkov

Adrian Powell

Simon Bubel

Yuri Khlebnikov

Elif Balkas

Steven Griffiths

Michael J Paisley

Caleb A Kent

Varad R Sakhalkar

Oleksandr Kramarenko

Eugene Deyneka

Jeffrey C Seaman

Curt Progl

Alexander Kevin Shveyd

Lukas Nattermann

Kathy Doverspike

Michael Fusco

Silicon carbide (SiC) wafers and related methods are disclosed that include large diameter SiC wafers with wafer shape characteristics suitable for semiconductor manufacturing. Large diameter SiC wafers are disclosed that have reduced deformation related to stress and strain effects associated with forming such SiC wafers. As described herein, wafer shape and flatness characteristics may be improved by reducing crystallographic stress profiles during growth of SiC crystal boules or ingots. Wafer shape and flatness characteristics may also be improved after individual SiC wafers have been separated from corresponding SiC crystal boules. In this regard, SiC wafers and related methods are disclosed that include large diameter SiC wafers with suitable crystal quality and wafer shape characteristics including low values for wafer bow, warp, and thickness variation.

Large diameter silicon carbide wafers

A method of analyzing semiconductor wafers includes capturing a first image of a first crystalline material, etching a first surface of the first crystalline material to delineate etch defects in the first crystalline material, and capturing a second image of first crystalline material after etching the first surface of the first crystalline material. Based on the second image, labels of etch defects delineated in the first surface of the first crystalline material are generated. The first image and the labels of etch defects are spatially coordinated to form a defect map identifying one or more defects in the first image based on the delineated etch defects, and based on the defect map and nondestructive data obtained from a second crystalline material, defects in the second crystalline material are identified.

Nondestructive characterization for crystalline wafers

An example method includes obtaining a workpiece image of a semiconductor workpiece. The example method includes providing the workpiece image as input to a machine-learned encoding model. The example method includes obtaining an output from the machine-learned encoding model, the output includes an encoding corresponding to the semiconductor workpiece. The example method includes determining one or more characteristics of the semiconductor workpiece based at least in part on the encoding or modifying a semiconductor manufacturing process based at least in part on the encoding.

Multi-scale autoencoders for semiconductor workpiece understanding

Silicon carbide (SiC) materials including SiC wafers and SiC boules and related methods are disclosed that provide large dimension SiC wafers with reduced crystallographic stress. Growth conditions for SiC materials include maintaining a generally convex growth surface of SiC crystals, adjusting differences in front-side to back-side thermal profiles of growing SiC crystals, supplying sufficient source flux to allow commercially viable growth rates for SiC crystals, and reducing the inclusion of contaminants or non-SiC particles in SiC source materials and corresponding SiC crystals. By forming larger dimension SiC crystals that exhibit lower crystallographic stress, overall dislocation densities that are associated with missing or additional planes of atoms may be reduced, thereby improving crystal quality and usable SiC crystal growth heights.

Large dimension silicon carbide single crystalline materials with reduced crystallographic stress

Nondestructive characterization of crystalline wafers is provided, including defect detection, identification, and counting. Certain aspects relate to development of nondestructive, high fidelity defect characterization and/or dislocation counting methods based on deep neural networks. Certain aspects relate to nondestructive methods for defect characterization of silicon carbide (SiC) wafers. By subjecting SiC wafers to nondestructive defect characterization, SiC wafers in their final state may be characterized and subsequently used for device fabrication, vastly reducing the expense of the characterization process. Nondestructive defect characterization also allows for increased sampling and improved feedback loops between crystalline growth process development and subsequent device production.

Wafer images and related alignment methods for crystalline wafers are disclosed. Certain aspects relate to accessing and aligning images of a same or similar crystalline wafer captured from different imaging sources. Alignment may include determining spatial differences between shared crystalline features in various wafer images of the same or similar crystalline wafer and transforming at least one of the images according to the determined spatial differences. With sufficient alignment, information may be associated and/or transferred between the various images, thereby providing the capability of forming a combined wafer image and sub-images thereof with high resolution and spatial coordination between different image sources. Certain aspects relate to development of nondestructive, high fidelity defect characterization and/or dislocation counting methods in crystalline materials based on modern deep convolutional neural networks (DCNN).

Alignment for wafer images

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Durham, NC, United States of America

Matthew David Conrad