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Isaac Yorke

Publications and source records attributed to Isaac Yorke.

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Photonic Memory as a Dynamical Phase for Neuromorphic Computing: A Unified Framework with Experimental Realization

Photonic memory underpins optical information processing, neuromorphic photonics, and photonic computing. Existing studies typically treat dispersive, nonlinear, and driven-dissipative memory as distinct physical phenomena, despite all being governed by the evolution of the optical field. This work proposes a unified phase-based framework in which photonic memory is interpreted as a dynamical phase, with transitions between memory regimes governed by optical phase evolution, Kerr nonlinearity, and the balance between delayed feedback and dissipation. Within this framework, dispersive memory arises from frequency-dependent phase accumulation, nonlinear memory emerges through intensity-dependent phase evolution leading to bistability and hysteresis, and driven-dissipative memory is established through attractor convergence and memory stabilization. The proposed framework is validated through theoretical analysis and numerical simulations, demonstrating a continuous progression from linear dispersive memory to nonlinear and ultimately driven-dissipative memory. Experimental validation is performed using a silicon photonic waveguide incorporating chirped Bragg gratings. Group delay measurements reveal distinct linear and nonlinear memory responses, while the reconstructed memory distribution demonstrates the coexistence of dispersive, nonlinear, and driven-dissipative memory within a single integrated photonic device. To the best of the author's knowledge, this constitutes the first experimental demonstration supporting the coexistence of all three photonic memory regimes in a single photonic platform. These results establish optical phase as the unifying physical quantity underlying photonic memory and provide a common framework for designing future neuromorphic photonic systems, reservoir computers, and integrated photonic processors.

physics.optics

Reconfigurable Nonlinear Photonic Networks for In-Situ Learning and Memory Formation via Driven-Dissipative Dynamics

Photonic neuromorphic computing offers a promising route to overcoming the limitations of conventional Von Neumann architectures by exploiting the high bandwidth, low latency, and massive parallelism of optical systems. However, most existing implementations rely on fixed dynamical substrates such as classic reservoir computing, where learning is restricted to external readout layers and memory is limited to transient fading effects. This work proposes a Reconfigurable Nonlinear Photonic Decision Network (RNPDN), a physically grounded neuromorphic framework in which computation, memory, and learning emerge directly from driven-dissipative dynamics. Through numerical simulations, this work demonstrates the simultaneous realization of key properties: local physical learning rules enabling adaptive state evolution, a tunable stability-plasticity tradeoff governed by decay and hysteresis mechanisms, controlled memory formation and erasure via bistable photonic states, fading memory, in-situ learning, and hardware-faithful nonlinear dynamics incorporating saturation and dissipation. In contrast to conventional approaches, the proposed system enables intrinsic adaptation within the physical layer while supporting both transient and persistent memory. These results establish a unified framework for adaptive photonic information processing and provide a pathway toward scalable and energy-efficient neuromorphic photonic hardware.

physics.optics