Heterojunction‐Driven Stochasticity: Bi‐Heterojunction Noise‐Enhanced Negative Transconductance Transistor in Image Generation
Why this work is in the frame
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Bibliographic record
Abstract
Abstract 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 GS = −11 V → −0.073 µS at V GS = −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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it