Reconfigurable Nonlinear Activation Using Low-Loss Ge3Sb2Te15 for Photonic Neural Networks.

The escalating demand for high-performance computing has spurred interest in integrated silicon photonics to overcome the constraints of traditional von Neumann architectures. Reconfigurable photonic devices with low optical loss, high-speed switching, and nonvolatile operation are essential especially for photonic neural networks. This study introduces a robust high-throughput methodology that serves as a primary engine for accelerating compositional search within the ternary Ge-Sb-Te (GST) system. Through this approach, we unveiled Ge3Sb2Te15 (GST3215), a novel optical phase-change material (OPCM) that mitigates the high optical losses inherent in conventional OPCMs like Ge2Sb2Te5 (GST225). With reduced extinction coefficients and an improved figure of merit, GST3215 seamlessly integrates into hybrid silicon micro-ring resonator (MRR), achieving lower insertion loss, a broader modulation range, and precise multilevel optical modulation-ideal traits for reconfigurable photonic systems. Moreover, GST3215 supports programmable nonlinear activation functions (e.g., ReLU, ELU, RBF) in a photonic neural network, lifting classification accuracy on the FashionMNIST dataset from 84.5% to 87.7%. These findings position GST3215 as a game-changer for large-scale photonic integration and optical computing.