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.