Predicting photolithography performance in silico for a given materials combination is essential for developing better patterning processes. However, it is still an extremely daunting task because of ...the entangled chemistry with multiple reactions among many material components. Herein, we investigated the EUV-induced photochemical reaction mechanism of a model photoacid generator (PAG), triphenylsulfonium cation, using atomiC–Scale materials modeling to elucidate that the acid generation yield strongly depends on two main factors: the lowest unoccupied molecular orbital (LUMO) of PAG cation associated with the electron-trap efficiency ‘before C–S bond dissociation’ and the overall oxidation energy change of rearranged PAG associated with the proton-generation efficiency ‘after C–S bond dissociation’. Furthermore, by considering stepwise reactions accordingly, we developed a two-parameter-based prediction model predicting the exposure dose of the resist, which outperformed the traditional LUMO-based prediction model. Our model suggests that one should not focus only on the LUMO energies but also on the energy change during the rearrangement process of the activated triphenylsulfonium (TPS) species. We also believe that the model is well suited for computational materials screening and/or inverse design of novel PAG materials with high lithographic performances.
Concentration controlled, nanocrystalline Er doped Y 2 SiO 5 particles were fabricated. Using 2-level model the cooperative upconversion coefficient was determined. We find that Er-doped YSO has much ...lower cooperative upconversion than silica, enabling it to achieve population inversion within a reasonable limit.
Optically active and electrically excitable silicon pillar structure is fabricated by electrochemical etching method. Silicon pillars structure is made by etching porous silicon layers in ...hydrofluoric acid-based solution. Afterwards, silicon pillars that are spin-coated with Er silicate show strong Er-related 1.53 mum photo luminescence.
We conducted this study to investigate the independent association of medial temporal atrophy (MTA) and white matter hyperintensities (WMH) with cognitive impairments of Alzheimer's disease (AD) ...patients and the interaction between MTA and WMH.
From 13 centers, a total of 216 AD patients were consecutively recruited and their MTA and WMH were visually rated. We evaluated the association of MTA and WMH with the various cognitive domains, and the interaction between MTA and WMH.
MTA independently correlated with scores of the Mini-Mental State Examination (MMSE), Clinical Dementia Rating scale (CDR), delayed recalls of the Seoul Verbal Learning Test (SVLT), the Boston Naming Test (BNT), and Word Fluency. WMH independently correlated with MMSE, CDR, Digit Span, and Stroop word reading, but not with delayed recall. There were interactions of WMH and MTA on CDR (p = 0.004), SVLT (p = 0.023), BNT (p = 0.002) and the semantic Word Fluency (p = 0.007).
MTA and WMH independently affected cognitive deficits in AD patients, with somewhat different patterns where MTA was associated mostly with memory and language, while WMH were associated with attention and frontal executive functions. This study also showed interactions between MTA and WMH on some cognitive deficits and dementia severity, suggesting that they synergistically contribute to cognitive impairment in AD.
Recently, a new training algorithm, multigradient, has been published for neural networks and it is reported that the multigradient outperforms the backpropagation when neural networks are used as a ...classifier. When neural networks are used as an equalizer in communications, they can be viewed as a classifier. In this paper, we apply the multigradient algorithm to train the neural networks that are used as equalizers. Experiments show that the neural networks trained using the multigradient noticeably outperforms the neural networks trained by the backpropagation.
