Poor aspect profiles of plasmonic lithography patterns are suffering from evanescent waves' scattering loss in metal films and decaying exposure in photoresist. To address this issue, we experimentally report plasmonic cavity lens to enhance aspect profile and resolution of plasmonic lithography. The profile depth of half-pitch (hp) 32 nm resist patterns is experimentally improved up to 23 nm, exceeding in the reported sub-10 nm photoresist depth. The resist patterns are then transferred to bottom resist patterns with 80 nm depth using hard-mask technology and etching steps. The resolution of plasmonic cavity lens up to hp 22 nm is experimentally demonstrated. The enhancement of the aspect profile and resolution is mainly attributed to evanescent waves amplifying from the bottom silver layer and scattering loss reduction with smooth silver films in plasmonic cavity lens. Further, theoretical near-field exposure model is utilized to evaluate aspect profile with plasmonic cavity lens and well illustrates the experimental results.

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In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.

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For near-field imaging optics, minimum resolvable feature size is highly constrained by the near-field diffraction limit associated with the illumination light wavelength and the air distance between the imaging devices and objects. In this study, a plasmonic cavity lens composed of Ag-photoresist-Ag form incorporating high spatial frequency spectrum off-axis illumination (OAI) is proposed to realize deep subwavelength imaging far beyond the near-field diffraction limit. This approach benefits from the resonance effect of the plasmonic cavity lens and the wavevector shifting behavior via OAI, which remarkably enhances the object’s subwavelength information and damps negative imaging contribution from the longitudinal electric field component in imaging region. Experimental images of well resolved 60-nm half-pitch patterns under 365-nm ultra-violet light are demonstrated at air distance of 80 nm between the mask patterns and plasmonic cavity lens, approximately four-fold longer than that in the conventional near-field lithography and superlens scheme. The ultimate air distance for the 60-nm half-pitch object could be theoretically extended to 120 nm. Moreover, two-dimensional L-shape patterns and deep subwavelength patterns are illustrated via simulations and experiments. This study promises the significant potential to make plasmonic lithography as a practical, cost-effective, simple and parallel nano-fabrication approach.

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