Patterning involves the set of process steps – including lithography, deposition, and etch – that create the extremely small, intricate features of an integrated circuit. With each new generation, device dimensions continue to shrink. For advanced structures, these feature sizes can be too small and/or packed too closely together for conventional lithography, the step that transfers the chip design’s intricate detail from the mask “template” onto the wafer. To compensate, chipmakers are using advanced techniques like double/quadruple and spacer-based patterning, involving multiple masks and process sets. Even as these approaches ease lithography limitations, they create new demands for exceptional process precision and film quality in order to accurately produce the fine, dense features required.
Our Solutions
Coronus systems focus on the bevel edge to enhance overall yield. Semiconductor processing can cause residues and roughness to accumulate along the wafer edge where they may flake off, drift to other areas, and create defects that cause a device to fail. Coronus etch products remove bevel residues and Coronus deposition protect the wafer bevel from damage.
Our Reliant deposition products enable roadmaps for Specialty Technologies and extend the productive life of fabs.
Line edge roughness (LER) is a variation in the width of a lithographic pattern along one edge of a structure inside a chip. Line edge roughness can be a critical variation source and defect mechanism in advanced logic and memory devices, and can lead to poor device performance or even device failure [1~3]. Deposition-etch cycling is an effective technique to reduce line edge roughness. In this study, we demonstrate how virtual fabrication can provide guidance on how to perform deposition/etch cycling in order to reduce LER.
In this article, we demonstrate a pathfinding technique for a novel Vertical DRAM technology. First, we identify important process parameters (defined by current semiconductor production equipment capabilities) that strongly impact yield. By using a virtual model, we then perform experimental optimization of the Vertical DRAM device across specific target ranges, to help predict and improve the yield of this next generation product.