Dow Chemical (China)

Since the invention of chemical amplified resist (CAR), the polymers in resist have been predominantly produced by free radical polymerization (FRP) with complicatedly elaborate process control, for the non-living character of reaction. Reversible addition fragmentation chain transfer (RAFT) technology provides highly controlled and living character for radical polymerization but has been overlooked in manufacture of CAR. As sizes of pattern become smaller, local variations in resist film are increasingly influential factors and need to be urgently addressed to meet further requirements of critical dimension uniformity (CDU) and line edge roughness (LER) in ArF and extreme ultraviolet (EUV) lithography. The advantages of RAFT polymerization match these demands perfectly as it can produce polymers in higher uniformity of both size and composition. On the other hand, although RAFT technique can provide polymers with high uniformity, it introduces an undesired dithio or trithio terminal group. The terminal group needs to be removed cautiously before applied in resist formulation for its weak but nontrivial instability. In this study, the heterogeneity sourced from polymerization is analyzed, resins meeting requirements of photoresist are prepared through RAFT polymerization, and the improvements of applying RAFT technique rather than FRP is discussed and evaluated.

Accurate prediction of polymer properties is essential for material design. However, traditional methods often struggle with efficiency due to the limited availability of experimental data and insufficient understanding of polymer microstructures, particularly in the case of small datasets for amorphous polymers. To address this hallenge, we introduce a novel paradigm based on "local clusters", structural motifs whose properties are efficiently computed using quantum chemistry (QC) methods. These clusters, simulated across multiple scales, serve as key descriptors that encapsulate essential microstructural features of polymers. By integrating these QC-derived descriptors with graph convolutional networks (GNNs) or neural networks (NNs), we have developed Locluster, a multiscale, microstructure-driven predictive model tailored for data-scarce environments in amorphous polymer research. Notably, Locluster requires only 2–5 descriptors and as few as two dozen training samples to accurately predict critical polymer properties, including density, refractive index, dielectric constant, and glass transition temperature. The model achieves predictive performance comparable to approaches trained on large datasets, thereby effectively mitigating the data scarcity challenge in polymer informatics. This work provides an effective option for the rational design and accelerated discovery of novel polymeric materials.

Electrically Conductive Silicone Composites (ECSCs) have become essential solutions for EMI shielding, electrical connectivity, and grounding in consumer electronics, automotive, aerospace, and telecommunications. Distinguished by their exceptional thermal stability, broad application temperature range, flexibility, and low flammability, ECSCs offer unique advantages in EMI shielding, with the added flexibility to incorporate thermal conductivity (TC) as needed. In this presentation, we introduce several novel ECSCs characterized by high electrical conductivity (EC) and shielding effectiveness (SE). These conductive silicone composites exhibit robust adhesion to various substrates and good dispensability, making them suitable for use as adhesives, gaskets, and sealants in electromagnetic compatibility (EMC) applications.

Detachable and degradable polymeric adhesives have demonstrated considerable value and significance, accompanying the increasing demands for waste recycling and the ongoing concern for green and sustainable development. Herein, a chain extender containing the bis(cyclohexanone) oxalyldihydrazone structure was introduced into polyurethanes, which achieved efficient degradation in an alkaline environment with the Mw¯ decreasing from 41.9 to 4.9 kDa. The shear strength of the linear polyurethane between polycarbonate specimens reduced by 98.5%, and on a wide range of substrates, the shear strength of both linear and cross-linked polyurethanes reduced by more than 65.0% as a joint result of the base and cosolvent. These degradable polyurethanes with adhesion properties have exhibited great potential to be used in the separation of multilayer flexible packaging, electronics assembly, and battery pack recycling.

GNN models on heterogeneous graphs have achieved state-of-the-art (SOTA) performance in various graph tasks such as link prediction and node classification. Despite their success in providing SOTA results, popular GNN libraries, such as PyG and DGL, fail to provide fast and efficient solutions for heterogeneous GNN models. One common key bottlenecks of models like RGAT, RGCN, and HGT is relation-specific linear projection. In this paper, we propose two high-performing tensor operators: gather-mm and segment-mm to address the issue. We demonstrate the effectiveness of the proposed operators in training two popular heterogeneous GNN models – RGCN and HGT. Our proposed approaches outperform the full-batch training time of RGCN by up to 3× and mini-batch by up to 2×.

Abstract Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment completes. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one nanometer resolution in a Au-Fe3O4 metamaterial, Co3O4 - Mn3O4 core-shell nanocrystals, and ZnS-Cu0.64S0.36 nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX / EELS) signals. Now sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials.

In this paper, a specially designed method for inspecting and categorizing whiskers in press-fit systems was established. Effect of PTH final hole diameter and pin coating thickness on whisker growth was evaluated. It was found that PTH final hole diameter showed more significant effect on whisker growth than pin coating thickness. The smaller the hole was, the more and longer whiskers grow in the press-fit system. However, thicker coating on pins introduced more scratched residue on annular rings and also could increase the chance of short circuit failure. The tin coating thickness on pins for telecom/datacom applications is recommended to between 0.5 μm and 2.5 μm.

A reliable approach for the determination of volatile oxygenated compounds in high-pressure liquefied hydrocarbons with post-column reaction gas chromatography has been developed.