silicon

本文探讨了学术界与产业界如何通过重视基础硅设计原理来适应AI原生时代的挑战和机遇，促进社区转型。

Addressing urgent demands for novel functional materials in energy, microelectronics, and biomedical applications, this work tackles the challenge of material design optimization under high-dimensional, discrete, sparse, and fragmented data conditions. Method: We propose a tightly integrated hybrid modeling paradigm that synergistically combines first-principles calculations, multi-fidelity modeling, graph neural networks, Bayesian optimization, and knowledge-guided transfer learning to jointly predict material properties and infer viable synthesis pathways. Contribution/Results: We systematically identify critical bottlenecks—including material data quality deficiencies and performance–application mismatches—previously uncharacterized. Evaluated on MIT’s benchmark material design task, our framework achieves efficient convergence from tens of thousands of candidates to a high-confidence top-10 set. It successfully discovers multiple new metal–insulator transition materials and proposes experimentally verifiable synthesis routes.

"This study addresses the challenges in data comparison and reutilization within the field of materials science, specifically due to the inconsistent reporting formats of atomic layer deposition (ALD) and atomic layer etching (ALE) experiments and simulations. The work proposes and expertly reviews four JSON schemas based on the QUDT standard, designed to standardize the description of materials, process conditions, configurations, and results in ALD/ALE processes, ensuring data consistency and interoperability. The innovation lies in the development and application of these specialized JSON schemas, which, through the use of a schema-miner toolset, enable effective extraction and structured representation of literature content, thereby promoting knowledge sharing. Additionally, the study provides a comparative analysis of the schemas and publishes related structured records on the ORKG platform, demonstrating their potential for guiding information extraction tasks."

This study addresses the challenge of performing in situ structural characterization of large-format combinatorial material libraries under high-temperature and controlled-atmosphere conditions, which existing high-throughput techniques struggle to achieve. The authors developed a wide-bore furnace capable of accommodating full 100 mm silicon wafers, integrated with synchrotron-based X-ray diffraction (XRD) and X-ray fluorescence (XRF), enabling in situ high-throughput analysis up to 735 °C across atmospheres ranging from nitrogen to pure oxygen. Combined with ternary oxide libraries fabricated via pulsed laser deposition and a custom MATLAB-based thermal expansion analysis program, this platform enabled, for the first time, wafer-scale in situ XRD characterization under realistic processing conditions. The approach overcomes limitations of conventional sample stages and reveals the inadequacy of Vegard’s law in predicting lattice behavior within high-entropy oxide systems.

This study addresses the semiconductor industry’s stringent demand for ultra-high-purity, multi-element materials—and the associated environmental and supply chain vulnerabilities. Moving beyond conventional mineral-resource perspectives, we propose a dual-dimensional analytical framework centered on “elemental diversity” and “purity,” integrating material provenance tracing with supply chain network analysis to systematically identify upstream critical material sources, latent stakeholders, and cross-sectoral dependencies. Our findings reveal that semiconductor manufacturing is profoundly reliant on the chemical industry for ultra-pure precursors and specialty reagents; supply chain reconfiguration has intensified both synergistic collaboration and systemic vulnerability with the foundational chemical sector. The study uncovers the long-overlooked material foundations of digital infrastructure and proposes a materials-flow-oriented policy intervention pathway and a novel industry-coordination paradigm to advance sustainable digital transformation.

This work addresses the tight coupling between design intent and printer-specific representations in heterogeneous manufacturing, which hinders cross-platform reuse. The authors propose a novel compiler architecture that models fabrication-aware design as a staged, type-directed lowering process, decoupling source design, attribute translation, and backend compilation to enable manufacturing-agnostic expression. Introducing compiler paradigms to heterogeneous manufacturing for the first time, the approach unifies volumetric information—such as material composition, hardness, and color—through implicit geometry and typed spatial attribute fields, automatically generating voxel stacks, G-code, or slicer projects. Experiments demonstrate successful fabrication of complex objects embedding CT data, Shore hardness fields, and full-color fields on both material jetting and extrusion platforms, validating cross-process reusability. The accompanying Python toolkit is publicly released.

This study addresses the lack of predictive correlations between fabrication conditions and functional behavior in oxide memristors by constructing a multiscale physics framework linking plasma deposition parameters to macroscopic device performance. Methodologically, it integrates large-scale statistical analysis, plasma and atomistic simulations, and data-driven clustering techniques to identify oxygen vacancy density as a critical latent variable. The core contribution lies in proposing a paradigm shift from deterministic defect engineering to probabilistic defect-state design. By revealing the underlying probabilistic cascade mechanism and elucidating how spatially heterogeneous subdomain integration drives variability in large-area devices, this work establishes a foundation for physics-informed, controllable memristor design.

This study addresses the absence of evaluation benchmarks for language model agents in two-dimensional material flake field-effect transistor (FET) layout assessment by constructing the first benchmark comprising 128 tasks. Agents are required to generate GDSII-formatted layouts from microscopic image contours, with a deterministic verifier introduced to ensure geometric compliance and contour integrity. Methodologically, models such as GPT5.6-Luna are integrated with ReAct-3 and Plan-and-Execute strategies, utilizing Python scripts to generate polygon paths rendered into GDSII format, thereby supporting multi-flake and hole-containing complex tasks for the first time. Experimental results demonstrate that the optimal configuration achieves an 80.5% solution rate, 43.8% consistency, and approximately 60% expert audit acceptance, significantly outperforming single-pass planning baselines. These findings validate both the solvability of this task and the effectiveness of the proposed benchmark.

This work addresses the inaccuracy in hotspot prediction during transient thermal simulation of heterogeneous back-end-of-line (BEOL) structures in 3D chip stacks. To overcome this challenge, the authors propose a transient multiscale thermal analysis framework that extends multiscale homogenization—previously limited to steady-state scenarios—to transient thermal modeling. The method automatically extracts layout structures from GDSII/OASIS files to construct representative volume elements (RVEs), and under the assumption of temperature-independent material properties, it derives homogenized thermal conductivity and volumetric heat capacity, yielding an analytical expression for effective transient thermal conductivity. Validated on a 1 mm × 1 mm SoC model with 5 μm and 10 μm RVEs, the approach accurately captures transient thermal behavior at a time step of dt = 0.001, significantly enhancing the fidelity of thermal modeling for three-dimensional heterogeneous BEOL architectures.

本文探讨了利用TCAD生成数据结合机器学习解决半导体器件设计与缺陷发现中的数据稀缺和高维度问题，展示了从简单机器学习到大语言模型的应用。