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

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.