BEOL Ferroelectric Compute-in-Memory Ising Machine for Simulated Bifurcation

Abstract

Computationally hard combinatorial optimization problems are pervasive in science and engineering, yet their NP-hard nature renders them increasingly inefficient to solve on conventional von Neumann architectures as problem size grows. Ising machines implemented using dynamical, digital and compute-in-memory (CiM) approaches offer a promising alternative, but often suffer from poor initialization and a fundamental trade-off between algorithmic performance and hardware efficiency. Hardware-friendly schemes such as simulated annealing converge slowly, whereas faster algorithms, including simulated bifurcation, are difficult to implement efficiently in CiM hardware, limiting both convergence speed and solution quality. To address these limitations, here we present a ferroelectric field-effect transistor (FeFET)-based CiM Ising framework that tightly co-designs algorithms and hardware to efficiently solve large-scale combinatorial optimization problems. The proposed approach employs a two-step algorithmic flow: an attention-inspired initialization that exploits global spin topology and reduces the required iterations by up to 80%, followed by a lightweight simulated bifurcation algorithm specifically tailored for CiM implementation. To natively accelerate the core vector-matrix and vector-matrix-vector operations in both steps, we fabricate a 32x256 FeFET CiM chip using ferroelectric capacitors integrated at the back end of line of a 180-nm CMOS platform. Across Max-Cut instances with up to 100,000 nodes, the proposed hardware-software co-designed solver achieves up to a 175.9x speedup over a GPU-based simulated bifurcation implementation while consistently delivering superior solution quality.