Probabilistic computing utilizing HfO₂-based stochastic ferroelectric tunnel junctions

Probabilistic neural networks are good at solving complex optimization tasks, but require stochastic, energy-efficient probabilistic bit (p-bit) neurons and reliable artificial synapses. Here we show stochastic ferroelectric tunnel junctions (s-FTJs) and reliable-FTJs (r-FTJs) by tuning the oxygen vacancy concentration in Hf_0.5Zr_0.5O₂ ferroelectric film, which are utilized to set up p-bit neurons and synapses, respectively. The s-FTJ-based p-bit outputs 0 or 1 with a tunable probability, and it can operate as a true random number generator at a probability of 0.5. The write power per p-bit is ~76 nW, significantly lower than other reported p-bit implementations. A hardware prototype of a four-neuron Boltzmann machine is experimentally constructed for probabilistic computing, which successfully solves the maximum independent set problem. Simulations show that a 655-neuron Boltzmann machine can accurately predict the secondary structure of a 64-nucleotide RNA. This work provides a high-performance probabilistic computing solution with low energy consumption and excellent process compatibility.