Abstract:This Letter reports the formation, evolution, and prevention of thermally induced defects on germanium upon high-temperature (up to 890 °C) vacuum (2×10−6 Torr or 2.67×10−6 mbar) annealing. It is found that the shape of defects evolves depending on the annealing temperature and duration. The defect shape can be classified into three groups (pyramid, expanded pyramid, and doughnut), considering the interplay of chemical desorption and surface diffusion. The effects of annealing conditions on the density and siz… Show more
“…The ramping rate of temperature was set to be 25 C/min. The cover of another cleaned Ge wafer was simply placed on the hole patterned sample to prevent the formation of thermally induced defects due to high temperature and high vacuum annealing 29 . Figure 1 ( a ) The shape evolution process of the GON structure.…”
Empty space in germanium (ESG) or germanium-on-nothing (GON) are unique self-assembled germanium structures with multiscale cavities of various morphologies. Due to their simple fabrication process and high-quality crystallinity after self-assembly, they can be applied in various fields including micro-/nanoelectronics, optoelectronics, and precision sensors, to name a few. In contrast to their simple fabrication, inspection is intrinsically difficult due to buried structures. Today, ultrasonic atomic force microscopy and interferometry are some prevalent non-destructive 3-D imaging methods that are used to inspect the underlying ESG structures. However, these non-destructive characterization methods suffer from low throughput due to slow measurement speed and limited measurable thickness. To overcome these limitations, this work proposes a new methodology to construct a principal-component-analysis based database that correlates surface images with empirically determined sub-surface structures. Then, from this database, the morphology of buried sub-surface structure is determined only using surface topography. Since the acquisition rate of a single nanoscale surface micrograph is up to a few orders faster than a thorough 3-D sub-surface analysis, the proposed methodology benefits from improved throughput compared to current inspection methods. Also, an empirical destructive test essentially resolves the measurable thickness limitation. We also demonstrate the practicality of the proposed methodology by applying it to GON devices to selectively detect and quantitatively analyze surface defects. Compared to state-of-the-art deep learning-based defect detection schemes, our method is much effortlessly finetunable for specific applications. In terms of sub-surface analysis, this work proposes a fast, robust, and high-resolution methodology which could potentially replace the conventional exhaustive sub-surface inspection schemes.
“…The ramping rate of temperature was set to be 25 C/min. The cover of another cleaned Ge wafer was simply placed on the hole patterned sample to prevent the formation of thermally induced defects due to high temperature and high vacuum annealing 29 . Figure 1 ( a ) The shape evolution process of the GON structure.…”
Empty space in germanium (ESG) or germanium-on-nothing (GON) are unique self-assembled germanium structures with multiscale cavities of various morphologies. Due to their simple fabrication process and high-quality crystallinity after self-assembly, they can be applied in various fields including micro-/nanoelectronics, optoelectronics, and precision sensors, to name a few. In contrast to their simple fabrication, inspection is intrinsically difficult due to buried structures. Today, ultrasonic atomic force microscopy and interferometry are some prevalent non-destructive 3-D imaging methods that are used to inspect the underlying ESG structures. However, these non-destructive characterization methods suffer from low throughput due to slow measurement speed and limited measurable thickness. To overcome these limitations, this work proposes a new methodology to construct a principal-component-analysis based database that correlates surface images with empirically determined sub-surface structures. Then, from this database, the morphology of buried sub-surface structure is determined only using surface topography. Since the acquisition rate of a single nanoscale surface micrograph is up to a few orders faster than a thorough 3-D sub-surface analysis, the proposed methodology benefits from improved throughput compared to current inspection methods. Also, an empirical destructive test essentially resolves the measurable thickness limitation. We also demonstrate the practicality of the proposed methodology by applying it to GON devices to selectively detect and quantitatively analyze surface defects. Compared to state-of-the-art deep learning-based defect detection schemes, our method is much effortlessly finetunable for specific applications. In terms of sub-surface analysis, this work proposes a fast, robust, and high-resolution methodology which could potentially replace the conventional exhaustive sub-surface inspection schemes.
“…After cleaning the hole patterned silicon wafer using the piranha solution and diluted HF solution, the sample is annealed at 1150 °C for 60 min (AR = 7) and 150 min (AR = 12) under a high vacuum condition (2 × 10 −6 Torr). [ 32 ] The ramping rate of the vacuum furnace is set to be 25 °C min −1 .…”
“…The ramping rate of temperature was set to be 25 • C/min. The cover of another cleaned Ge wafer was simply placed on the hole patterned sample to prevent the formation of thermally induced defects due to high temperature and high vacuum annealing 28 .…”
Empty Space in Germanium (ESG) or Germanium-on-Nothing (GON) are unique self-assembled germanium structures with multiscale cavities of various morphologies. Due to their simple fabrication process and high-quality crystallinity after self-assembly, they can be applied in various fields including micro-/nanoelectronics, optoelectronics, and precision sensors, to name a few. In contrast to their simple fabrication, inspection is intrinsically difficult due to buried structures. Today, ultrasonic atomic force microscopy and interferometry are some prevalent non-destructive 3-D imaging methods that are used to inspect the underlying ESG structures. However, these non-destructive characterization methods suffer from low throughput due to slow measurement speed and limited measurable thickness. To overcome these limitations, this work proposes a new methodology to construct a principal-component-analysis based database that correlates surface images with empirically determined sub-surface structures by interpolating the surface topography from the database and determining the buried sub-surface structure. Since the acquisition rate of a single nanoscale surface micrograph is up to a few orders faster than a thorough 3D sub-surface analysis, the proposed methodology would provide an exploitable and decisive advantage over the currently prevalent methods. Also, an empirical destructive test essentially resolves the measurable thickness limitation. We also demonstrate the practicality of the proposed methodology by applying it to GON devices to selectively detect and quantitatively analyze surface defects. Compared to state-of-the-art deep learning-based defect detection schemes, our method is much effortlessly finetunable for specific applications. In terms of sub-surface analysis, this work proposes a fast, robust, and high-resolution methodology which could potentially replace the conventional exhaustive sub-surface inspection schemes.
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