Multi‐State Probabilistic Computing Using Floating‐Body MOSFETs Based on the Potts Model for Solving Complex Combinatorial Optimization Problems

Probabilistic computing has gained attention for solving combinatorial optimization problems (COPs), mainly using the Ising model, which may not be suitable for complex COPs. Instead, this work proposes a multi-state probabilistic computing system based on the Potts model using stochastic threshold switching floating-body metal-oxide-semiconductor field-effect transistors (FB-MOSFETs) as the multi-state probabilistic bits (p-bits) to solve challenging COPs. The system employs drain voltage sharing and a one-hot sampling method to achieve controllable probabilistic behavior and scalable annealing. Experimental validations on spin glass and max-4-cut problems demonstrate that the system efficiently samples a tunable Boltzmann distribution while converging faster than traditional methods. Comparative analyses further highlight superior energy efficiency and decreased time-to-solution, underscoring the potential of multi-state probabilistic computing for large-scale, complex COPs using only MOSFET devices.