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Probabilistic computing utilizing HfO2-based stochastic ferroelectric tunnel junctions
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DOI：10.1038/s41467-026-72742-9.png)
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Abstract
En 中文
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 Hf0.5Zr0.5O2 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. Probabilistic computing excels in complex tasks, but existing hardware needs separate components for conflicting deterministic and stochastic operations. Guan et al. use oxygen vacancy-regulated FTJs for dual-mode functionality, validated by a prototype and RNA structure prediction simulations with superior energy efficiency.
Keywords:
probabilistic computing
stochastic ferroelectric tunnel junctions
p-bit neurons
Boltzmann machine
energy-efficient hardware
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