Search for: All records

The integration of computing with memory is essential for distributed, massively parallel, and adaptive architectures such as neural networks in artificial intelligence (AI). Accelerating AI can be achieved through photonic computing, but it requires nonvolatile photonic memory capable of rapid updates during on-chip training sessions or when new information becomes available during deployment. Phase-change materials (PCMs) are promising for providing compact, nonvolatile optical weighting; however, they face limitations in terms of bit precision, programming speed, and cycling endurance. Here, we propose a novel photonic memory cell that merges nonvolatile photonic weighting using PCMs with high-speed, volatile tuning enabled by an integrated PN junction. Our experiments demonstrate that the same PN modulator, fabricated via a foundry-compatible process, can achieve dual functionality. It supports coarse programmability for setting initial optical weights and facilitates high-speed fine-tuning to adjust these weights dynamically. The result shows a 400-fold increase in volatile tuning speed and a 10,000-fold enhancement in efficiency. This multifunctional photonic memory with volatile and nonvolatile capabilities could significantly advance the performance and versatility of photonic memory cells, providing robust solutions for dynamic computing environments. 

Abstract Nonvolatile photonic integrated circuits employing phase change materials have relied either on optical switching mechanisms with precise multi-level control but poor scalability or electrical switching with seamless integration and scalability but mostly limited to a binary response. Recent works have demonstrated electrical multi-level switching; however, they relied on the stochastic nucleation process to achieve partial crystallization with low demonstrated repeatability and cyclability. Here, we re-engineer waveguide-integrated microheaters to achieve precise spatial control of the temperature profile (i.e., hotspot) and, thus, switch deterministic areas of an embedded phase change material cell. We experimentally demonstrate this concept using a variety of foundry-processed doped-silicon microheaters on a silicon-on-insulator platform to trigger multi-step amorphization and reversible switching of Sb₂Se₃and Ge₂Sb₂Se₄Te alloys. We further characterize the response of our microheaters using Transient Thermoreflectance Imaging. Our approach combines the deterministic control resulting from a spatially resolved glassy-crystalline distribution with the scalability of electro-thermal switching devices, thus paving the way to reliable multi-level switching towards robust reprogrammable phase-change photonic devices for analog processing and computing. 

We experimentally demonstrate the first electrically programmable, non-volatile silicon photonic content addressable memory cell using Sb₂Se₃phase change material on microring resonators, opening the path for light-based search operations in zero-power look-up tables. 

Bragg gratings offer high-performance filtering and routing of light on-chip through a periodic modulation of a waveguide’s effective refractive index. Here, we model and experimentally demonstrate the use of Sb₂Se₃, a nonvolatile and transparent phase-change material, to tune the resonance conditions in two devices which leverage periodic Bragg gratings—a stopband filter and Fabry-Perot cavity. Through simulations, we show that similar refractive indices between silicon and amorphous Sb₂Se₃can be used to induce broadband transparency, while the crystalline state can enhance the index contrast in these Bragg devices. Our experimental results show the promise and limitations of this design approach and highlight specific fabrication challenges which need to be addressed in future implementations. 

Abstract Advancements in nanofabrication processes have propelled nonvolatile phase change materials (PCMs) beyond storage‐class applications. They are now making headway in fields such as photonic integrated circuits (PIC), free‐space optics, and plasmonics. This shift is owed to their distinct electrical, optical, and thermal properties between their different atomic structures, which can be reversibly switched through thermal stimuli. However, the reliability of PCM‐based optical components is not yet on par with that of storage‐class devices. This is in part due to the challenges in maintaining a uniform temperature distribution across the PCM volume during phase transformation, which is essential to mitigate stress and element segregation as the device size exceeds a few micrometers. Understanding thermal transport in PCM‐based devices is thus crucial as it dictates not only the durability but also the performance and power consumption of these devices. This article reviews recent advances in the development of PCM‐based photonic devices from a thermal transport perspective and explores potential avenues to enhance device reliability. The aim is to provide insights into how PCM‐based technologies can evolve beyond storage‐class applications, maintain their functionality, and achieve longer lifetimes. 

Abstract Chalcogenide phase change materials (PCMs) have become one of the most promising material platforms for the Optics and Photonics community. The unparalleled combination of nonvolatility and large optical property modulation promises devices with low‐energy consumption and ultra‐compact form factors. At the core of all these applications lies the difficult task of precisely controlling the glassy amorphous and crystalline domains that compose the PCM microstructure and dictate the optical response. A spatially controllable glassy‐crystalline domain distribution is desired for intermediate optical response (vs. binary response between fully amorphous and crystalline states), and temporally resolved domains are sought after for repeatable reconfiguration. In this perspective, we briefly review the fundamentals of PCM phase transition in various reconfiguring approaches for optical devices. We discuss each method's underpinning mechanisms, design, advantages, and downsides. Finally, we lay out current challenges and future directions in this field.