Experimental investigation of silicon and silicon nitride platforms for phase-change photonic in-memory computing

Xuan Li,Harish Bhaskaran,Nathan Youngblood,Emanuele Gemo,Zengguang Cheng,C David Wright,Santiago Garcia-Cuevas Carrillo,Wolfram H P Pernice

doi:10.1364/optica.379228

Abstract

Advances in artificial intelligence have greatly increased demand for data-intensive computing. Integrated photonics is a promising approach to meet this demand in big-data processing due to its potential for wide bandwidth, high speed, low latency, and low-energy computing. Photonic computing using phase-change materials combines the benefits of integrated photonics and co-located data storage, which of late has evolved rapidly as an emerging area of interest. In spite of rapid advances of demonstrations in this field on both silicon and silicon nitride platforms, a clear pathway towards choosing between the two has been lacking. In this paper, we systematically evaluate and compare computation performance of phase-change photonics on a silicon platform and a silicon nitride platform. Our experimental results show that while silicon platforms are superior to silicon nitride in terms of potential for integration, modulation speed, and device footprint, they require trade-offs in terms of energy efficiency. We then successfully demonstrate single-pulse modulation using phase-change optical memory on silicon photonic waveguides and demonstrate efficient programming, memory retention, and readout of > --> 4 bits of data per cell. Our approach paves the way for in-memory computing on the silicon photonic platform.

Highlights

Demands for applying silicon (Si) photonics to high-performance computing systems have grown significantly in recent years due to the breakdown of Dennard scaling [1] and information transfer bottlenecks in the conventional von Neumann architecture [2,3]
Phase-change optical memory (PCOM) photonics has been applied to photonic computing and artificial neural networks, such as implementation of accumulative addition [38], multiplication [39], optical synapses [40], and most recently, deep-learning neurosynaptic networks for image recognition [41]
We quantitatively explore the differences between PCOM cells on Si and silicon nitride (SiN) platforms and highlight the advantages and disadvantages of both platforms

Summary

INTRODUCTION

Demands for applying silicon (Si) photonics to high-performance computing systems have grown significantly in recent years due to the breakdown of Dennard scaling [1] and information transfer bottlenecks in the conventional von Neumann architecture [2,3]. Data processing on photonic platforms is a promising approach because of the potential advantages over electrical approaches, where large bandwidth, high efficiency, ultrafast modulation speed, and low cross talk are crucial [4] Critical photonic components, such as lasers [5], modulators [6], switches [7], filters [8], multiplexers [9,10], photodetectors [11,12,13,14,15], and memory cells, have been developed on different material platforms, such as silicon nitride (SiN), GeSi, III-V semiconductors, and others [16,17,18], with silicon-on-insulator (SOI) being the dominant platform. There has been work on developing phase-change photonics using GST on Si waveguides [35,42,43] Both Si and SiN have been employed, these two platforms have very distinct optical properties as well as relative advantages, both in the linear and nonlinear regimes. Our experimental results and analysis serve to provide a roadmap for this rapidly growing field

Material and Device Structure Comparison

Dynamic Response during Amorphization

Switching Threshold and Energy Consumption

Demonstration of Single-Pulse Recrystallization on Si

CONCLUSION