In viscoelastic materials, individually short-lived bonds collectively result in a mechanical resistance which is long lived but finite as, ultimately, cracks appear. Here, we provide a microscopic mechanism by which a critical crack length emerges from the nonlinear local bond dynamics. Because of this emerging length scale, macroscopic viscoelastic materials fracture in a fundamentally different manner from microscopically small systems considered in previous models. We provide and numerically verify analytical equations for the dependence of the critical crack length on the bond kinetics and applied stress.

Metamaterials are man‐made materials which get their properties from their structure rather than their chemical composition. Their mesostructure is specifically designed to create functionalities not found in nature. However, despite the broad variety of metamaterials developed in recent years, a straightforward procedure to design these complex materials with tailored properties has not yet been established. Here, the inverse design problem is tackled by introducing a general optimization tool to explore the range of material properties that can be achieved. Specifically, a stochastic optimization algorithm is applied and its applicability to disjoint problems is demonstrated, with a focus on tuning the buckling properties of mechanical metamaterials, including experimental verification of the predictions. Besides this problem, this algorithm can be applied to a large variety of systems that, because of their complexity, would be challenging otherwise. Potential applications range from the design of optomechanical resonators, acoustic band gap materials, to dielectric metasurfaces.

One of the main challenges in robotics is the development of systems that can adapt to their environment and achieve autonomous behavior. Current approaches typically aim to achieve this by increasing the complexity of the centralized controller by, e.g., direct modeling of their behavior, or implementing machine learning. In contrast, we simplify the controller using a decentralized and modular approach, with the aim of finding specific requirements needed for a robust and scalable learning strategy in robots. To achieve this, we conducted experiments and simulations on a specific robotic platform assembled from identical autonomous units that continuously sense their environment and react to it. By letting each unit adapt its behavior independently using a basic Monte Carlo scheme, the assembled system is able to learn and maintain optimal behavior in a dynamic environment as long as its memory is representative of the current environment, even when incurring damage. We show that the physical connection between the units is enough to achieve learning, and no additional communication or centralized information is required. As a result, such a distributed learning approach can be easily scaled to larger assemblies, blurring the boundaries between materials and robots, paving the way for a new class of modular “robotic matter” that can autonomously learn to thrive in dynamic or unfamiliar situations, for example, encountered by soft robots or self-assembled (micro)robots in various environments spanning from the medical realm to space explorations.

Scatterometry is an optical metrology technique in which light scattered from a specifically designed grating stack (overlay target) is measured in the far-field. Using 1D periodic overlay target designs, the technique has been shown to have nanometer-scale sensitivity to spatial misalignments of subsequent patterned layers, which are also known as overlay errors. However, while scatterometry is highly sensitive to overlay errors, multiple sources of systematic errors hinder its absolute accuracy. Here, we investigate how an extended version of scatterometry called Fourier scatterometry, in combination with more complex overlay target designs, can help addressing those challenges. To this end, we developed a statistical method that can determine the influence of 2D overlay targets on the overlay measurement uncertainty. We study periodic and deterministic aperiodic designs as well as designs that emerged from simulated annealing optimizations. Our results suggest that current overlay target designs could be augmented by more complex 2D designs to fulfill specific purposes, such as fabrication robustness and high sensitivity over a large overlay range.