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May 8, 2026Nature Communications0 citationsOpen Access

Probabilistic computing utilizing HfO2-based stochastic ferroelectric tunnel junctions

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ZGZeyu GuanHZHansheng ZhuYLYaoxin Li

Key Points

  • The aim is to develop a high-performance probabilistic computing solution using HfO2-based stochastic ferroelectric tunnel junctions.
  • Developed a hardware prototype of a four-neuron Boltzmann machine for probabilistic computing.
  • Conducted simulations for a 655-neuron Boltzmann machine to predict RNA secondary structures.
  • Measured power consumption per p-bit at ~76 nW.
  • Successfully solved the maximum independent set problem with the prototype machine.
  • Achieved accurate predictions of RNA secondary structure with the simulated 655-neuron model.

Abstract

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.

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Cite This Study

Guan et al. (2026) studied this question.

synapsesocial.com/papers/69fd7d4abfa21ec5bbf05da6https://doi.org/10.1038/s41467-026-72742-9
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