Van der Waals Ferroelectric CuInP ₂ S ₆ ‐based Multi‐slope In‐memory Probabilistic Computing

Abstract

ABSTRACT Probabilistic bit (p‐bit) is the fundamental building block and core element of probabilistic computing (p‐computing). However, physical separation of bit generation and memory storage creates a memory bottleneck in conventional p‐computing architectures. We report on experimentally integrating voltage‐tunable stochastic bit generation and non‐volatile memory functionalities within a single in‐memory device to realize a p‐bit with van der Waals ferroelectric CuInP 2 S 6 (CIPS). Leveraging the stochastic displacement of Cu + ions and the material's remanent polarization under an external electric field, the proposed device achieves stable random bit retention (>1000 s) with low power consumption (∼75 nW). This eliminates the need for data transfer between separate memory and logic units, thereby enabling efficient in‐memory p‐computing with improved system‐level performance. In‐memory p‐computing outperforms conventional p‐computing in device‐to‐system‐level NP‐hard simulations, reducing time‐complexity from O(n 2 ) to O(n 1.5 ). Notably, the sigmoid slope of the probabilistic output is dynamically tuned by varying the CIPS layer thickness, enabling adaptive control over exploration–exploitation characteristics. Broader slopes facilitate initial exploration, whereas steeper slopes support rapid convergence in later stages. Sigmoid slope tunability over a wide dynamic range (6.17–38.41) reduces convergence steps by 400‐fold, highlighting the potential of CIPS‐based p‐bit as a compact, energy‐efficient platform for scalable and adaptive p‐computing.