Deep Learning-Based High Throughput Inspection in 3D Nanofabrication and Defect Reversal in Nanopillar Arrays: Implications for Next Generation Transistors

Abstract

Densely packed high-aspect-ratio (HAR) nanostructures are the core elements of future microelectronics components. Manufacturing these nanostructures for device applications requires multiple fabrication steps involving wet processes, followed by a drying step. During drying, these nanostructures experience strong capillary forces that induce their bending and cause them to permanently stick to their neighbors, a phenomenon often referred to as pattern collapse. The pattern collapse and the difficulty in reliably identifying damaged nanostructures pose a critical challenge for the fabrication of HAR devices. Here, we developed a machine learning-based approach to identify collapsed nanostructures from a large patterned array of vertical Si nanopillars with 99.84% accuracy. Furthermore, we show that the pattern collapse can be reversed by selectively etching the native surface SiO2 layer of the nanopillars at their adhesions. Our approach for accurate and rapid identification of the collapsed nanostructures combined with the method to reverse this damage provides a versatile platform for developing high-yield fabrication processes for nanoscale semiconductor devices.