Heterogeneous Molecular‐Silicon Integration for Precision Neuromorphic Computing

ABSTRACT

While an expanding repertoire of materials exhibits neuromorphic functionality, translating these advances into scalable hardware integrated with silicon remains a formidable challenge. Monolithic integration, though ultimately desirable, is rarely tractable at early stages of material development. Progress is therefore often assessed through idealized simulations that overlook system‐level constraints and non‐idealities such as device variability, mixed‐signal conversions, peripheral circuit noise, and energy overhead, yielding projections that can mislead. Here we present a heterogeneously integrated platform as a necessary stepping‐stone toward scalable molecular‐silicon hardware—a 64 × 64 molecular crossbar coupled with custom Si‐circuitry in which every interface and signal pathway is explicitly defined and directly measured, yielding a realistic map of the design space for further scale‐up. We demonstrate a one‐step vector–matrix multiplication with > 12‐bit precision, ∼73 dB signal‐to‐noise ratio, ∼80‐ns write speeds, and high write accuracy across 10 000 programming cycles without correction loops, a substantial advance over the state of the art in dot‐product engines. Using this platform, we execute workloads spanning signal and image processing, biomarker detection and tracking, and cryptographic operations. This work establishes the foundation on which subsequent scale‐up strategies, including monolithic molecular–silicon integration, can be designed and assessed.