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Riikka L. Puurunen

Publications and source records attributed to Riikka L. Puurunen.

3 recordsLinked to original sources

Enhancing Conformality in Atomic Layer Deposition through Low Growth Per Cycle

Atomic layer deposition (ALD) is a key enabling technology for advanced microelectronics as it enables the growth of functional thin films on complex three-dimensional substrates with unmatched atomic-scale precision. Conformal film growth in high-aspect-ratio (HAR) structures is limited by slow diffusion of reactant molecules into deep, narrow features. This work elucidates an approach to grow conformal films faster by investigating the relationship between the ALD growth per cycle (GPC) and the film penetration depth in HAR structures through modeling and experiments. Diffusion-reaction simulations reveal, under Knudsen diffusion conditions, an inverse square root relationship between the film penetration depth and GPC. The prediction is validated experimentally with a model zinc oxide ALD process on rectangular lateral HAR structures, using an inhibitor molecule to decrease the GPC. While ALD is traditionally optimized for "high GPC," this work shows that "low GPC" may increase efficiency when conformality is key.

cond-mat.mtrl-sci↗

Publishing FAIR and Machine-actionable Reviews in Materials Science: The Case for Symbolic Knowledge in Neuro-symbolic Artificial Intelligence

Scientific reviews are central to knowledge integration in materials science, yet their key insights remain locked in narrative text and static PDF tables, limiting reuse by humans and machines alike. This article presents a case study in atomic layer deposition and etching (ALD/E) where we publish review tables as FAIR, machine-actionable comparisons in the Open Research Knowledge Graph (ORKG), turning them into structured, queryable knowledge. Building on this, we contrast symbolic querying over ORKG with large language model-based querying, and argue that a curated symbolic layer should remain the backbone of reliable neurosymbolic AI in materials science, with LLMs serving as complementary, symbolically grounded interfaces rather than standalone sources of truth.

cs.AI↗

Atomic layer deposition on particulate materials from 1988 through 2023: A quantitative review of technologies, materials and applications

Atomic layer deposition (ALD) is widely studied for numerous applications and is commercially employed in the semiconductor industry, where planar substrates are the norm. However, the inherent ALD feature of coating virtually any surface geometry with atomistic thickness control is equally attractive for coating particulate materials (supports). In this review, we provide a comprehensive overview of the developments in this decades-old field of ALD on particulate materials, drawing on a bottom-up and quantitative analysis of 799 articles from this field. The obtained dataset is the basis for abstractions regarding reactor types (specifically for particles), coating materials, reactants, supports and processing conditions. Furthermore, the dataset enables direct access to specific processing conditions (for a given material, surface functionality, application etc.) and increases accessibility of the respective literature. We also review fundamental concepts of ALD on particles, and discuss the most common applications, i.e., catalysis (thermo-, electro-, photo-), batteries, luminescent phosphors and healthcare. Finally, we identify historical trends, and provide an outlook on prospective developments.

cond-mat.mtrl-sci↗