Natural materials such as wood exhibit high mechanical properties through cellulose structured at multiple length scales and embedded in a matrix of similar chemical structure. These hierarchical materials have inspired the design of lightweight composites composed of naturally occurring polymers. However, the close proximity of melt and decomposition temperature remain a challenge. In this work, cellulose propionate (CP) is modified to reduce its glass transition temperature and melt viscosity, allowing its use as a matrix in a natural fibre-reinforced composite. Through better impregnation, the modified CP matrix composites showed an increase in stiffness and strength of ∼10% and 20%, respectively, in comparison to unmodified CP matrix composites. The impact properties also increased by up to 28%, showing that modified CP is a credible matrix for realising sustainable all-cellulose natural fibre composites with high stiffness, strength and toughness.

Particle defects are important contributors to yield loss in semi-conductor manufacturing. Particles need to be detected and characterized in order to determine and eliminate their root cause. We have conceived a process flow for advanced defect classification (ADC) that distinguishes three consecutive steps; detection, review and classification. For defect detection, TNO has developed the Rapid Nano (RN3) particle scanner, which illuminates the sample from nine azimuth angles. The RN3 is capable of detecting 42 nm Latex Sphere Equivalent (LSE) particles on XXX-flat Silicon wafers. For each sample, the lower detection limit (LDL) can be verified by an analysis of the speckle signal, which originates from the surface roughness of the substrate. In detection-mode (RN3.1), the signal from all illumination angles is added. In review-mode (RN3.9), the signals from all nine arms are recorded individually and analyzed in order to retrieve additional information on the shape and size of deep sub-wavelength defects. This paper presents experimental and modelling results on the extraction of shape information from the RN3.9 multi-azimuth signal such as aspect ratio, skewness, and orientation of test defects. Both modeling and experimental work confirm that the RN3.9 signal contains detailed defect shape information. After review by RN3.9, defects are coarsely classified, yielding a purified Defect-of-Interest (DoI) list for further analysis on slower metrology tools, such as SEM, AFM or HIM, that provide more detailed review data and further classification. Purifying the DoI list via optical metrology with RN3.9 will make inspection time on slower review tools more efficient.