Heterogeneous integration of phase change materials (PCM) into photonic integrated circuits is of current interest for all-optical signal processing and photonic in-memory computing. The basic building block consists of waveguides or resonators embedded with state-switchable PCM cells evanescently coupled to the optical mode. Despite recent advances, further improvements are desired in performance metrics like switching speeds, switching energies, device footprint, and fan-out. We propose an architecture using resonant metamaterial waveguides loaded with Ge2Sb2Te5 (GST) nanoantenna, and present a numerical study of its performance. Our proposed design is predicted to have a write energy of 16 pJ, an erase energy of 190 pJ (which is three to four times lower than previous reports), and, an order of magnitude improvement in the write-process figure-of-merit. Additional advantages include lowered ON state insertion loss and GST volume reduction.

.In recent years, there has been a growing interest in active metasurfaces. In particular, phase change material-based metasurfaces offering all-optical reconfigurability are being explored. Despite recent progress, further improvement in device reconfiguration energies and optical contrast achievable between the amorphous and crystalline states is desirable. In this work, we demonstrate that using a mirror-backed chalcogenide-based narrowband perfect absorber metasurface can significantly improve the device’s reflection contrast at much lower energies than its mirrorless case. By considering a GST225 metasurface operating in the near IR, our systematic numerical study finds improved reflection contrast (up to −32  dB, Q-factor 19.22 compared with 9.59 dB, Q-factor 11 for the mirrorless case). For the mirrored case, the thermal study finds faster crystallization (up to 6 times) at reduced reconfiguration thresholds (72 times lower) compared with the mirrorless case. This results in a more than 2 orders of magnitude higher device figure of merit [defined as the change in reflection contrast (in dB) to a corresponding change in optical energy (in nJ)] compared with the mirrorless case. The results are promising for high-performance metasurfaces at reduced switching energies.

.The capabilities of modern precision nanofabrication and the wide choice of materials [plasmonic metals, high-index dielectrics, phase change materials (PCM), and 2D materials] make the inverse design of nanophotonic structures such as metasurfaces increasingly difficult. Deep learning is becoming increasingly relevant for nanophotonics inverse design. Although deep learning design methodologies are becoming increasingly sophisticated, the problem of the simultaneous inverse design of structure and material has not received much attention. In this contribution, we propose a deep learning-based inverse design methodology for simultaneous material choice and device geometry optimization. To demonstrate the utility of the proposed method, we consider the topical problem of active metasurface design using PCMs. We consider a set of four commonly used PCMs in both fully amorphous and crystalline material phases for the material choice and an arbitrarily specifiable polygonal meta-atom shape for the geometry part, which leads to a vast structure/material design space. We find that a suitably designed deep neural network can achieve good optical spectrum prediction capability in an ample design space. Furthermore, we show that this forward model has a sufficiently high predictive ability to be used in a surrogate-optimization setup resulting in the inverse design of active metasurfaces of switchable functionality.

No abstract