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Electrically tunable optical devices present diverse functionalities for manipulating electromagnetic waves by leveraging elements capable of reversibly switching between different optical states. This adaptability in adjusting their responses to electromagnetic waves after fabrication is crucial for developing more efficient and compact optical systems for a broad range of applications, including sensing, imaging, telecommunications, and data storage. Chalcogenide‐based phase‐change materials (PCMs) have shown great promise due to their stable, nonvolatile phase transition between amorphous and crystalline states. Nonetheless, optimizing the switching parameters of PCM devices and maintaining their stable operation over thousands of cycles with minimal variation can be challenging. Herein, the critical role of PCM pattern as well as electrical pulse form in achieving reliable and stable switching is reported on, extending the operational lifetime of the device beyond 13000 switching events. To achieve this, a computer‐aided algorithm that monitors optical changes in the device and adjusts the applied voltage in accordance with the phase transformation process is developed, thereby significantly enhancing the lifetime of these reconfigurable devices. The findings reveal that patterned PCM structures show significantly higher endurance compared to blanket PCM thin films.
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Learning to learn by using nonequilibrium training protocols for adaptable materials
Evolution in time-varying environments naturally leads to adaptable biological systems that can easily switch functionalities. Advances in the synthesis of environmentally responsive materials therefore open up the possibility of creating a wide range of synthetic materials which can also be trained for adaptability. We consider high-dimensional inverse problems for materials where any particular functionality can be realized by numerous equivalent choices of design parameters. By periodically switching targets in a given design algorithm, we can teach a material to perform incompatible functionalities with minimal changes in design parameters. We exhibit this learning strategy for adaptability in two simulated settings: elastic networks that are designed to switch deformation modes with minimal bond changes and heteropolymers whose folding pathway selections are controlled by a minimal set of monomer affinities. The resulting designs can reveal physical principles, such as nucleation-controlled folding, that enable such adaptability.
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- PAR ID:
- 10427366
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 120
- Issue:
- 27
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.2219558120
