Optimization and Control of Large Block Copolymer Self-Assembly via Precision Solvent Vapor Annealing

High Aspect Ratio Nanoscale Pores through BCP-Based Metal Oxide Masks and Advanced Dry Etching

The reliable and regular modification of the surface properties of substrates plays a crucial role in material research and the development of functional surfaces. A key aspect of this is the development of the surface pores and topographies. These can confer specific advantages such as high surface area as well as specific functions such as hydrophobic properties. Here, we introduce a combination of nanoscale self-assembled block-copolymer-based metal oxide masks with optimized deep reactive ion etching (DRIE) of silicon to permit the fabrication of porous topographies with aspect ratios of up to 50. Following the evaluation of our procedure and involved parameters using various techniques, such as AFM or SEM, the suitability of our features for applications relying on high light absorption as well as efficient thermal management is explored and discussed in further detail.

Highly Ordered Porous Inorganic Structures via Block Copolymer Lithography: An Application of the Versatile and Selective Infiltration of the "Inverse" P2VP-b-PS System

A facile and versatile strategy was developed to produce highly ordered porous metal oxide structures via block copolymer (BCP) lithography. Phase separation of poly(2-vinylpyridine)-b-polystyrene (P2VP-b-PS) was induced by solvent vapor annealing in a nonselective solvent environment to fabricate cylindrical arrays. In this work, we thoroughly analyzed the effects of the film thickness, solvent annealing time, and temperature on the ordering of a P2VP-majority system for the first time, resulting in "inverse" structures. Reflectometry, atomic force microscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy (XPS), and transmission electron microscopy were used to characterize the formation of the highly ordered BCP morphology and the subsequently produced metal oxide film. At 40 min solvent annealing time, hexagonally close packed structures were produced with cylinder diameters ∼40 nm. Subsequently, the BCP films were infiltrated with different metal cations. Metal ions (Cr, Fe, Ni, and Ga) selectively infiltrated the P2VP domain, while the PS did not retain any detectable amount of metal precursor. This gave rise to a metal oxide porous structure after a UV/ozone (UVO) treatment. The results showed that the metal oxide structures demonstrated high fidelity compared to the BCP template and cylindrical domains presented a similar size to the previous PS structure. Moreover, XPS analyses revealed the complete elimination of the BCP template and confirmed the presence of the metal oxides. These metal oxides were used as hard masks for pattern transfer via dry etching as a further application. Silicon nanopores were fabricated mimicking the BCP template and demonstrated a pore depth of ∼50 nm. Ultimately, this strategy can be applied to create different inorganic nanostructures for a diverse range of applications, for example, solar cells, diodes, and integrated circuits. Furthermore, by optimizing the etching parameters, deeper structures can be obtained via ICP/RIE processes, leading to many potential applications.

Using Machine Learning to Predict and Understand Complex Self-Assembly Behaviors of a Multicomponent Nanocomposite

Blends of nanoparticles, polymers, and small molecules can self-assemble into optical, magnetic, and electronic devices with structure-dependent properties. However, the relationship between a multicomponent nanocomposite's formulation and its assembled structure is complex and cannot be predicted by theory. The blends can be strongly influenced by processing conditions, which can introduce non-equilibrium states. Currently, nanocomposite devices are designed through cycles of experimental trial and error. Machine learning (ML) methods are a compelling alternative because they can use existing datasets to map high-dimensional spaces. These methods do not rely on known relationships between parameters, so they are suited to complex systems without a solid theoretical foundation. Here, a dataset of 595 microscopy images of nanocomposite thin films is used to train a series of ML models. Correlations between the input and output parameters are examined, providing new insights into the system. Finally, the most successful ML model is used to predict the structures of new nanocomposite compositions. The results confirm that ML techniques can be used to improve the efficiency of nanocomposite device design. More broadly, the current study suggests some of the advantages and challenges associated with applying ML to complex systems.

Solvent-assisted self-assembly of block copolymer thin films

Solvent-assisted block copolymer self-assembly is a compelling method for processing and advancing practical applications of these materials due to the exceptional level of the control of BCP morphology and significant acceleration of ordering kinetics. Despite substantial experimental and theoretical efforts devoted to understanding of solvent-assisted BCP film ordering, the development of a universal BCP patterning protocol remains elusive; possibly due to a multitude of factors which dictate the self-assembly scenario. The aim of this review is to aggregate both seminal reports and the latest progress in solvent-assisted directed self-assembly and to provide the reader with theoretical background, including the outline of BCP ordering thermodynamics and kinetics phenomena. We also indicate significant BCP research areas and emerging high-tech applications where solvent-assisted processing might play a dominant role.

Room Temperature Fabrication of Macroporous Lignin Membranes for the Scalable Production of Black Silicon

Rising global demand for biodegradable materials and green sources of energy has brought attention to lignin. Herein, we report a method for manufacturing standalone lignin membranes without additives for the first time to date. We demonstrate a scalable method for macroporous (∼100 to 200 nm pores) lignin membrane production using four different organosolv lignin materials under a humid environment (>50% relative humidity) at ambient temperatures (∼20 °C). A range of different thicknesses is reported with densely porous films observed to form if the membrane thickness is below 100 nm. The fabricated membranes were readily used as a template for Ni²⁺ incorporation to produce a nickel oxide membrane after UV/ozone treatment. The resultant mask was etched via an inductively coupled plasma reactive ion etch process, forming a silicon membrane and as a result yielding black silicon (BSi) with a pore depth of >1 μm after 3 min with reflectance <3% in the visible light region. We anticipate that our lignin membrane methodology can be readily applied to various processes ranging from catalysis to sensing and adapted to large-scale manufacturing.

Thin film block copolymer self-assembly for nanophotonics

The nanophotonic engineering of light-matter interactions has profoundly changed research behind the design and fabrication of optical materials and devices. Metasurfaces-arrays of subwavelength nanostructures that interact resonantly with electromagnetic radiation-have emerged as an integral nanophotonic platform for a new generation of ultrathin lenses, displays, polarizers and other devices. Their success hinges on advances in lithography and nanofabrication in recent decades. While existing nanolithography techniques are suitable for basic research and prototyping, issues of cost, throughput, scalability, and substrate compatibility may preclude their use for many metasurface applications. Patterning via spontaneous self-assembly of block copolymer thin films offers an enticing alternative for nanophotonic manufacturing that is rapid, inexpensive, and applicable to large areas and diverse substrates. This review discusses the advantages and disadvantages of block copolymer-based nanopatterning and highlights recent progress in their use for broadband antireflection, surface enhanced Raman spectroscopy, and other nanophotonic applications. Recent advances in diversification of self-assembled block copolymer nanopatterns and improved processes for enhanced scalability of self-assembled nanopatterning using block copolymers are also discussed, with a spotlight on directions for future research that would enable a wider array of nanophotonic applications.

Pathway-Dependent Grain Coarsening of Block Copolymer Patterns under Controlled Solvent Evaporation

Solvent evaporation annealing (SEA) is a straightforward, single-step casting and annealing method of block copolymers (BCP) processing yielding large-grained morphologies in a very short time. Here, we present a quantitative analysis of BCP grain-coarsening in thin films under controlled evaporation of the solvent. Our study is aimed at understanding time and BCP concentration influence on the rate of the lateral growth of BCP grains. By systematically investigating the coarsening kinetics at various BCP concentrations, we observed a steeply decreasing exponential dependence of the kinetics power-law time exponent on polymer concentration. We used this dependence to formulate a mathematical model of BCP ordering under nonstationary conditions and a 2D, time- and concentration-dependent coarsening rate diagram, which can be used as an aid in engineering the BCP processing pathway in SEA and also in other directed self-assembly methods that utilize BCP-solvent interactions such as solvent vapor annealing.

Defining Swelling Kinetics in Block Copolymer Thin Films: The Critical Role of Temperature and Vapour Pressure Ramp

We studied the kinetics of swelling in high-χ lamellar-forming poly(styrene)-block- poly(lactic acid) (PS-b-PLA) block copolymer (BCP) by varying the heating rate and monitoring the solvent vapour pressure and the substrate temperature in situ during solvo-thermal vapour annealing (STVA) in an oven, and analysing the resulting morphology. Our results demonstrate that there is not only a solvent vapour pressure threshold (120 kPa), but also that the rate of reaching this pressure threshold has a significant effect on the microphase separation and the resulting morphologies. To study the heating rate effect, identical films were annealed in a tetrahydrofuran (THF) vapour environment under three different ramp regimes, low (rT<1 °C/min), medium (24 °C/min), for 60, 90 and 120 min, respectively, while the solvent vapour pressure and the substrate temperature were measured in real time. The translational order improved significantly with increasing the heating rate. The solvent mass uptake calculated for the different ramp regimes during annealing is linearly proportional to time, indicating that the swelling kinetics followed Case II diffusion. Two stages of the swelling behaviour were observed: (i) diffusion at the initial stages of swelling and (ii) stress relaxation, controlled at later stages. Films with a faster rate of increase in vapour pressure (rP>2 kPa/min) reached the pressure threshold value at an early stage of the swelling and attained a good phase separation. According to our results, highly ordered patterns are only obtained when the volume fraction of the solvent exceeds the polymer volume fraction, i.e., (φs≥φp), during the swelling process, and below this threshold value (φs=0.5), the films did not obtain a good structural order, even at longer annealing times.

Self-Aligned Assembly of a Poly(2-vinylpyridine)-b-Polystyrene-b-Poly(2-vinylpyridine) Triblock Copolymer on Graphene Nanoribbons

Directed self-assembly (DSA) of block copolymers is one of the most promising patterning techniques for patterning sub-10 nm features. However, at such small feature sizes, it is becoming increasingly difficult to fabricate the guiding pattern for the DSA process, and it is necessary to explore alternative guiding methods for DSA to achieve long-range ordered alignment. Here, we report the self-aligned assembly of a triblock copolymer, poly(2-vinylpyridine)-b-polystyrene-b-poly(2-vinylpyridine) (P2VP-b-PS-b-P2VP) on neutral graphene nanoribbons with the gap consisting of a P2VP-preferential silicon oxide (SiO₂) substrate via solvent vapor annealing. The assembled P2VP-b-PS-b-P2VP demonstrated long-range, one-dimensional alignment on the graphene substrate in a direction perpendicular to the boundary of the graphene and substrate with a half-pitch size of 8 nm, which greatly alleviates the lithography resolution required for traditional chemoepitaxy DSA. A wide processing window is demonstrated with the gap between graphene stripes varying from 10 to 100 nm, overcoming the restriction on widths of guiding patterns to have commensurate domain spacing. When the gap was reduced to 10 nm, P2VP-b-PS-b-P2VP formed a straight-line pattern on both the graphene and the substrate. Monte Carlo simulations showed that the self-aligned assembly of the triblock copolymer on the graphene nanoribbons is guided at the boundary of parallel and perpendicular lamellae on graphene and SiO₂, respectively. Simulations also indicate that the swelling of a system allows for rapid rearrangement of chains and quickly anneal any misaligned grains and defects. The effect of the interaction strength between SiO₂ and P2VP on the self-assembly is systematically investigated in simulations.

Solvent Vapor Annealing, Defect Analysis, and Optimization of Self-Assembly of Block Copolymers Using Machine Learning Approaches

Self-assembly of block copolymers (BCPs) is an alternative patterning technique that promises high resolution and density multiplication with lower costs. The defectivity of the resulting nanopatterns remains too high for many applications in microelectronics and is exacerbated by small variations of processing parameters, such as film thickness, and fluctuations of solvent vapor pressure and temperature, among others. In this work, a solvent vapor annealing (SVA) flow-controlled system is combined with design of experiments (DOE) and machine learning (ML) approaches. The SVA flow-controlled system enables precise optimization of the conditions of self-assembly of the high Flory-Huggins interaction parameter (χ) hexagonal dot-array forming BCP, poly(styrene-b-dimethylsiloxane) (PS-b-PDMS). The defects within the resulting patterns at various length scales are then characterized and quantified. The results show that the defectivity of the resulting nanopatterned surfaces is highly dependent upon very small variations of the initial film thicknesses of the BCP, as well as the degree of swelling under the SVA conditions. These parameters also significantly contribute to the quality of the resulting pattern with respect to grain coarsening, as well as the formation of different macroscale phases (single and double layers and wetting layers). The results of qualitative and quantitative defect analyses are then compiled into a single figure of merit (FOM) and are mapped across the experimental parameter space using ML approaches, which enable the identification of the narrow region of optimum conditions for SVA for a given BCP. The result of these analyses is a faster and less resource intensive route toward the production of low-defectivity BCP dot arrays via rational determination of the ideal combination of processing factors. The DOE and machine learning-enabled approach is generalizable to the scale-up of self-assembly-based nanopatterning for applications in electronic microfabrication.

Deep learning-based estimation of Flory-Huggins parameter of A-B block copolymers from cross-sectional images of phase-separated structures

In this study, deep learning (DL)-based estimation of the Flory-Huggins χ parameter of A-B diblock copolymers from two-dimensional cross-sectional images of three-dimensional (3D) phase-separated structures were investigated. 3D structures with random networks of phase-separated domains were generated from real-space self-consistent field simulations in the 25-40 χN range for chain lengths (N) of 20 and 40. To confirm that the prepared data can be discriminated using DL, image classification was performed using the VGG-16 network. We comprehensively investigated the performances of the learned networks in the regression problem. The generalization ability was evaluated from independent images with the unlearned χN. We found that, except for large χN values, the standard deviation values were approximately 0.1 and 0.5 for A-component fractions of 0.2 and 0.35, respectively. The images for larger χN values were more difficult to distinguish. In addition, the learning performances for the 4-class problem were comparable to those for the 8-class problem, except when the χN values were large. This information is useful for the analysis of real experimental image data, where the variation of samples is limited.

Electrolysis on a Chip with Tunable Thin Film Nanostructured PGM Electrocatalysts Generated from Self-Assembled Block Copolymer Templates

Self-assembled block copolymers are promising templates for fabricating thin film materials with tuned periodic feature sizes and geometry at the nanoscale. Here, a series of nanostructured platinum and iridium oxide electrocatalysts templated from poly(styrene)-block-poly(vinyl pyridine) (PSbPVP) block copolymers via an incipient wetness impregnation (IWI) pathway is reported. Both nanowire and nanocylinder electrocatalysts of varying feature sizes are assessed and higher catalyst loadings are achieved by the alkylation of the pyridine moieties in the PVP block prior to IWI. Electrocatalyst evaluations featuring hydrogen pump and water electrolysis demonstrations are carried out on interdigitated electrode (IDE) chips flexible with liquid supporting electrolytes and thin film polymer electrolytes. Notably, the mass activities of the nanostructured electrocatalysts from alkylated block copolymer templates are 35%-94% higher than electrocatalysts from non-alkylated block copolymer templates. Standing cylinder nanostructures lead to higher mass activities than lamellar variants despite their not having the largest surface area per unit catalyst loading demonstrating that mesostructure architectures have a profound impact on reactivity. Overall, IDE chips with model thin film electrocatalysts prepared from self-assembled block copolymers offer a high-throughput experimental method for correlating electrocatalyst nanostructure and composition to electrochemical reactivity.

Green Nanofabrication Opportunities in the Semiconductor Industry: A Life Cycle Perspective

The turn of the 21st century heralded in the semiconductor age alongside the Anthropocene epoch, characterised by the ever-increasing human impact on the environment. The ecological consequences of semiconductor chip manufacturing are the most predominant within the electronics industry. This is due to current reliance upon large amounts of solvents, acids and gases that have numerous toxicological impacts. Management and assessment of hazardous chemicals is complicated by trade secrets and continual rapid change in the electronic manufacturing process. Of the many subprocesses involved in chip manufacturing, lithographic processes are of particular concern. Current developments in bottom-up lithography, such as directed self-assembly (DSA) of block copolymers (BCPs), are being considered as a next-generation technology for semiconductor chip production. These nanofabrication techniques present a novel opportunity for improving the sustainability of lithography by reducing the number of processing steps, energy and chemical waste products involved. At present, to the extent of our knowledge, there is no published life cycle assessment (LCA) evaluating the environmental impact of new bottom-up lithography versus conventional lithographic techniques. Quantification of this impact is central to verifying whether these new nanofabrication routes can replace conventional deposition techniques in industry as a more environmentally friendly option.

Sluggish growth of poly(ε-caprolactone) leads to petal-shaped aggregates packed with thick-stack lamellar bundles

Kinetically sluggish growth of poly(ε-caprolactone) leads to peculiar camellia-petal-like bands on top, where a stereo-dissection demonstrates interior self-assembled lamellae being periodically grating-structured.