Ionic Driven Waveguide Integrated Memristor

Photonic memristors are anticipated to emerge as a novel hardware platform for neural network computing systems owing to their broad bandwidth communication capabilities and potential compatibility with non-von Neumann neuromorphic computing architectures. However, current photonic memristors are limited to a monotonic modulation mechanism and most demonstrate only a non-volatile temporal response scale, which restricts their applicability across more diverse neural network computing architectures. Here, we present a novel strategy that employs ion-doped chalcogenide glass, combined with a multi-dimensional modulation mechanism of optical and electrical fields, to realize the monolithic integration of non-volatile waveguide-integrated memristors featuring multi-level storage capacity and compact footprint, along with volatile reconfigurable memristors exhibiting high extinction ratios and excellent short-term plasticity. Furthermore, leveraging the powerful nonlinear dynamic response of the reconfigurable memristor, we developed an on-chip photonic reservoir computing system that operates without a feedback loop. This work provides a novel developmental approach for the development of neuron devices with varying time response scales and offers substantial support for neural networks in more accurately simulating brain functions.