Evidence map›Paper›PMID 41700537›Full record

ArticleAdvanced materials (Deerfield Beach, Fla.)2026

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

Sunwoo Cheong, Soo Hyung Lee, Janguk Han, Jun-Young Park, Dong Hoon Shin, Yoon Ho Jang, Sung Keun Shim, Sungho Kim, Cheol Seong Hwang, Joon-Kyu Han

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced materials (Deerfield Beach, Fla.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Probabilistic Computing via Gate-Tunable Random Telegraph Noise.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  2. NbOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Sunwoo CheongCollege of Engineering, Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, Republic of Korea.
Soo Hyung LeeCollege of Engineering, Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, Republic of Korea.
Janguk HanCollege of Engineering, Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, Republic of Korea.
Jun-Young ParkCollege of Engineering, Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, Republic of Korea.
Dong Hoon ShinCollege of Engineering, Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, Republic of Korea.
Yoon Ho JangCollege of Engineering, Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, Republic of Korea.
Sung Keun ShimCollege of Engineering, Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, Republic of Korea.
Sungho KimCollege of Engineering, Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, Republic of Korea.
Cheol Seong HwangCollege of Engineering, Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-6254-9758
Joon-Kyu HanCollege of Engineering, Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, Republic of Korea.

Funding

National Research Foundation of Korea 2020R1A3B2079882National Research Foundation of Korea RS-2024-00334953
6 · The paper itself

Abstract

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.

Indexed as

annealingcombinatorial optimizationfloating body MOSFETmulti‐stateprobabilistic computing

Identifiers

PMID41700537
PMCPMC12994325

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.