Heterojunction‐Driven Stochasticity: Bi‐Heterojunction Noise‐Enhanced Negative Transconductance Transistor in Image Generation
Reliable true-random number generator (TRNG) hardware demands amplified intrinsic noise and multi-bit entropy output, which are difficult to achieve in conventional single-device TRNG implementation. A bi-heterojunction noise-enhanced negative transconductance (BHN-NTC) transistor is presented, incorporating an asymmetric PTCDI-C13 layer into an NTC transistor. This design enhances electron injection, expanding the NTC region (19 → 27 V) and increasing negative transconductance (-0.036 µS at V<sub>GS</sub> = -11 V → -0.073 µS at V<sub>GS</sub> = -15 V) by reducing the electron injection barrier (≈2.13 eV → ≈0.41 eV). The bi-heterojunction configuration introduces a strong correlation between noises, including trapping/detrapping and generation/recombination processes. This property enables a threefold higher entropy throughput in TRNG, achieving a 3-bit output per sampling event. The BHN-NTC-driven TRNG leverages increased noise-induced entropy to generate more diverse latent vectors, mitigating mode collapse and enabling the synthesis of high-quality, realistic images. This significantly enhances StyleGAN2-based image generation, improving performance metrics such as Frechet inception distance (FID) (18.7 → 8.3), kernel inception distance (KID) (0.024 → 0.009), inception score (IS) (6.5 → 9.2), and multi-scale structural similarity (MS-SSIM) (0.43 → 0.21). Consequently, the BHN-NTC transistor establishes a scalable stochastic noise platform, advancing applications in secure electronics and probabilistic stochastic computing.
Authors
- Wonjun Shin (ORCID: https://orcid.org/0000-0001-9122-2458)
- Ryun‐Han Koo (ORCID: https://orcid.org/0000-0002-5708-3308)
- J. Q. Song (ORCID: https://orcid.org/0000-0001-7314-8573)
- Eun Kwang Lee (ORCID: https://orcid.org/0000-0001-5727-5716)
- Hocheon Yoo (ORCID: https://orcid.org/0000-0003-0772-3327)
- Chang‐Hyun Kim (ORCID: https://orcid.org/0000-0002-7112-6335)
- Young-Min Han (ORCID: https://orcid.org/0009-0004-5361-2366)
Institutions
- Seoul National University (KR)
- University of Ottawa (CA)
- Hanyang University (KR)
- Sungkyunkwan University (KR)
- Pukyong National University (KR)
- Anyang University (KR)
Publication Details
- Journal
- Advanced Materials
- Published
- 2025-06-25
- DOI
- https://doi.org/10.1002/adma.202505150
- Citations
- 6
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 4.38
Funders
- National Research Foundation
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea
- Institute for Information and Communications Technology Promotion