  All-optical and fully reconfigurable diffractive optical neural network
(DONN) architectures are promising for high-throughput and energy-efficient
machine learning (ML) hardware accelerators for broad applications. However,
current device and system implementations have limited performance. This work
demonstrates a novel diffractive device architecture, which is named digitized
heterostack and consists of multiple layers of nonvolatile phase change
materials (PCMs) with different thicknesses. This architecture can both
leverage the advantages of PCM optical properties and mitigate challenges
associated with implementing multilevel operations in a single PCM layer.
Proof-of-concept experiments demonstrate the electrical tuning of one PCM layer
in a spatial light modulation device, and thermal analysis guides the design of
DONN devices and systems to avoid thermal crosstalk if individual heterostacks
are assembled into an array. Further, heterostacks containing three PCM layers
are designed to have a large phase modulation range and uniform coverage and
the ML performance of DONN systems with designed heterostacks is evaluated. The
developed device architecture provides new opportunities for desirable
energy-efficient, fast-reconfigured, and compact DONN systems in the future.
