Molecular Mechanism of Selective Al2O3 Atomic Layer Deposition on Self-Assembled Monolayers.

Area-selective atomic layer deposition (AS-ALD) of insulating metallic oxide layers could be a useful nanopatterning technique for making increasingly complex semiconductor circuits. Although the alkanethiol self-assembled monolayer (SAM) has been considered promising as an ALD inhibitor, the low inhibition efficiency of the SAM during ALD processes makes its wide application difficult. We investigated the deposition mechanism of Al2O3 on alkanethiol-SAMs using temperature-dependent vibrational sum-frequency-generation spectroscopy. We found that the thermally induced formation of gauche defects in the SAMs is the main causative factor deteriorating the inhibition efficiency. Here, we demonstrate that a discontinuously temperature-controlled ALD technique involving self-healing and dissipation of thermally induced stress on the structure of SAM substantially enhances the SAM's inhibition efficiency and enables us to achieve 60 ALD cycles (6.6 nm). We anticipate that the present experimental results on the ALD mechanism on the SAM surface and the proposed ALD method will provide clues to improve the efficiency of AS-ALD, a promising nanoscale patterning and manufacturing technique.

Thermoresponse of Odd-Even Effect in n-Alkanethiolate Self-Assembled Monolayers on Gold Substrates.

This study examines thermoresponse of odd-even effect in self-assembled monolayers (SAMs) of n-alkanethiolates (SCn , n=3-18) formed on template-stripped gold (AuTS ) using macro- and microscopic analytical techniques, contact angle goniometry (CAG) and vibrational sum frequency generation (VSFG) spectroscopy, respectively. Both CAG and VSFG analyses showed that the odd-even effect in liquid-like SAMs (n=3-9) disappeared upon heating at 50-70 °C, indicating that the heating led to increased structural disorder regardless of odd and even carbon numbers. In contrast, the opposite thermoresponse was observed for odd and even SCn molecules in wax- and solid-like SAMs (n=10-18). Namely, temperature-dependent orientational change of terminal CH3 relative to the surface normal was opposite for the odd and even molecules, thereby leading to mitigated odd-even effect. Our work offers important insights into thermoresponse of supramolecular structure in condensed organic matter.

Honeycomb Artifact Removal Using Convolutional Neural Network for Fiber Bundle Imaging.

We present a new deep learning framework for removing honeycomb artifacts yielded by optical path blocking of cladding layers in fiber bundle imaging. The proposed framework, HAR-CNN, provides an end-to-end mapping from a raw fiber bundle image to an artifact-free image via a convolution neural network (CNN). The synthesis of honeycomb patterns on ordinary images allows conveniently learning and validating the network without the enormous ground truth collection by extra hardware setups. As a result, HAR-CNN shows significant performance improvement in honeycomb pattern removal and also detailed preservation for the 1961 USAF chart sample, compared with other conventional methods. Finally, HAR-CNN is GPU-accelerated for real-time processing and enhanced image mosaicking performance.