The fascinating story of olefin (or alkene) metathesis (eq
1) began almost five decades ago, when Anderson and
Merckling reported the first carbon-carbon double-bond
rearrangement reaction in the titanium-catalyzed polymerization of norbornene. Nine years later, Banks and Bailey reported “a new disproportionation reaction . . . in which olefins are converted to homologues of shorter and longer carbon chains...”. In 1967, Calderon and co-workers named this metal-catalyzed redistribution of carbon-carbon double bonds olefin metathesis, from the Greek word “μeτάθeση”, which means change of position. These contributions have since served as the foundation for an amazing research field, and olefin metathesis currently represents a powerful transformation in chemical synthesis, attracting a vast amount of interest both in industry and academia.

https://core.ac.uk/download/pdf/4884240.pdf

Ruthenium-based heterocyclic carbene-coordinated olefin metathesis catalysts.

: The unpolarized absorption and circular dichroism spectra of the fundamental vibrational transitions of the chiral molecule, 4-methyl-2-oxetanone, are calculated ab initio. Harmonic force fields are obtained using Density Functional Theory (DFT), MP2, and SCF methodologies and a 5S4P2D/3S2P (TZ2P) basis set. DFT calculations use the Local Spin Density Approximation (LSDA), BLYP, and Becke3LYP (B3LYP) density functionals. Mid-IR spectra predicted using LSDA, BLYP, and B3LYP force fields are of significantly different quality, the B3LYP force field yielding spectra in clearly superior, and overall excellent, agreement with experiment. The MP2 force field yields spectra in slightly worse agreement with experiment than the B3LYP force field. The SCF force field yields spectra in poor agreement with experiment.The basis set dependence of B3LYP force fields is also explored: the 6-31G* and TZ2P basis sets give very similar results while the 3-21G basis set yields spectra in substantially worse agreements with experiment. jg

Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields

Large-scale applications of transition metal-catalyzed couplings for the synthesis of pharmaceuticals.

In recent years, olefin cross metathesis (CM) has emerged as a powerful and convenient synthetic technique in organic chemistry; however, as a general synthetic method, CM has been limited by the lack of predictability in product selectivity and stereoselectivity. Investigations into olefin cross metathesis with several classes of olefins, including substituted and functionalized styrenes, secondary allylic alcohols, tertiary allylic alcohols, and olefins with α-quaternary centers, have led to a general model useful for the prediction of product selectivity and stereoselectivity in cross metathesis. As a general ranking of olefin reactivity in CM, olefins can be categorized by their relative abilities to undergo homodimerization via cross metathesis and the susceptibility of their homodimers toward secondary metathesis reactions. When an olefin of high reactivity is reacted with an olefin of lower reactivity (sterically bulky, electron-deficient, etc.), selective cross metathesis can be achieved using fee...

A General Model for Selectivity in Olefin Cross Metathesis

A mean-field phase diagram for conformationally symmetric diblock melts using the standard Gaussian polymer model is presented. Our calculation, which traverses the weak- to strong-segregation regimes, is free of traditional approximations. Regions of stability are determined for disordered (DIS) melts and for ordered structures including lamellae (L), hexagonally packed cylinders (H), body-centered cubic spheres (QIm3m), close-packed spheres (CPS), and the bicontinuous cubic network with Ia3d symmetry (QIa3d). The CPS phase exists in narrow regions along the order−disorder transition for χN ≥ 17.67. Results suggest that the QIa3d phase is not stable above χN ∼ 60. Along the L/QIa3d phase boundaries, a hexagonally perforated lamellar (HPL) phase is found to be nearly stable. Our results for the bicontinuous Pn3m cubic (QPn3m) phase, known as the OBDD, indicate that it is an unstable structure in diblock melts. Earlier approximation schemes used to examine mean-field behavior are reviewed, and compa...

Unifying Weak- and Strong-Segregation Block Copolymer Theories

A viable pattern customization strategy is a critical to continue fin pitch scaling. Analysis shows that a self-aligned customization scheme will be required for fin pitch scaling beyond 20nm. In this paper, we explore scaling of the Tone-Inverted Grapho-Epitaxy technique with 24nm pitch PS-b-PMMA polymer to create groups of fins with self-aligned spaces in between. We discuss material selection, self-aligned customization, and etch processes to form 24-nm-pitch fins on silicon on insulator substrates. We demonstrate two-dimensional pattern customization at 24nm pitch, confirming scalability of this approach. FinFET device integration results at both 28 and 24 nm pitches shows a promising path for continued fin pitch scaling.

Self-aligned line-space pattern customization with directed self-assembly graphoepitaxy at 24nm pitch

Directed self-assembly (DSA) has been proposed as apotential “bottom-up” technology to augment or extend thecapabilities of traditional and/or next-generation “top-down”lithography and overcome limitations with respect to resolu-tion, uniformity, process complexity, and throughput. In parti-cular, many promising capabilities of block copolymer-basedDSA, including the ability to make sublithographic patternswith feature multiplication/interpolation, have been reported.Despite considerable advancements in recent years, muchis still unproven and unknown regarding the capabilities(and limitations) of DSA as a viable lithographic technology.As DSA attempts to make the jump from exploratoryscience to a real-world technology, one should keep inmindtheoldidiom“thedevilisinthedetails.”Itisthesedetailswhich are likely to be of interest to the readership of JM

https://www.spiedigitallibrary.org/journals/Journal-of-MicroNanolithography-MEMS-and-MOEMS/volume-11/issue-3/031301/Special-Section-Guest-Editorial-Directed-Self-Assembly/10.1117/1.JMM.11.3.031301.pdf

Special Section Guest Editorial: Directed Self-Assembly

The present invention relates to a composition comprising a photoresist polymer and a fluoropolymer. In one embodiment, the fluoropolymer comprises a first monomer having a pendant group selected from alicyclic bis-hexafluoroisopropanol and aryl bis-hexafluoroisopropanol and preferably a second monomer selected from fluorinated styrene and fluorinated vinyl ether. The invention composition has improved receding contact angles with high refractive index hydrocarbon fluids used in immersion lithography and, thereby, provides improved performance in immersion lithography.

Photoresist Compositions and Methods of Use in High Index Immersion Lithography

Provided are sulfonamide-containing compositions, topcoat polymers, and additive polymers for use in lithographic processes that have improved static receding water contact angles over those known in the art. The sulfonamide-containing topcoat polymers and additive polymers of the present invention include sulfonamide-substituted repeat units with branched linking group as shown in Formula (I).

Topcoat composition comprising a polymer containing a repeat unit including a sulfonamide function, photoresist composition containing the same and methods of use

Designing novel materials that possess desired properties is a central need across many manufacturing industries. Driven by that industrial need, a variety of algorithms and tools have been developed that combine AI (machine learning and analytics) with domain knowledge in physics, chemistry, and materials science. AI-driven materials design can be divided to mainly two stages; the first one is the modeling stage, where the goal is to build an accurate regression or classification model to predict material properties (e.g. glass transition temperature) or attributes (e.g. toxic/non-toxic). The next stage is design, where the goal is to assemble or tune material structures so that they can achieve user-demanded target property values based on a prediction model that is trained in the modeling stage. For maximum benefit, these two stages should be architected to form a coherent workflow. Today there are several emerging services and tools for AI-driven material design, however, most of them provide only partial technical components (e.g. data analyzer, regression model, structure generator, etc.), that are useful for specific purposes, but for comprehensive material design, those components need to be orchestrated appropriately. Our material design system provides an end-to-end solution to this problem, with a workflow that consists of data input, feature encoding, prediction modeling, solution search, and structure generation. The system builds a regression model to predict properties, solves an inverse problem on the trained model, and generates novel chemical structure candidates that satisfy the target properties. In this paper we will introduce the methodology of our system, and demonstrate a simple example of inverse design generating new chemical structures that satisfy targeted physical property values.

Daniel P. Sanders

Papers

Self-aligned line-space pattern customization with directed self-assembly graphoepitaxy at 24nm pitch

Special Section Guest Editorial: Directed Self-Assembly

Photoresist Compositions and Methods of Use in High Index Immersion Lithography

Topcoat composition comprising a polymer containing a repeat unit including a sulfonamide function, photoresist composition containing the same and methods of use

AI-driven Inverse Design System for Organic Molecules.