异质结驱动的随机性:双异质结噪声增强负跨导晶体管在图像生成中的应用。

IF 26.8 1区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Youngmin Han, Ryun-Han Koo, Jaechan Song, Chang-Hyun Kim, Eun Kwang Lee, Wonjun Shin, Hocheon Yoo
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引用次数: 0

摘要

可靠的真随机数发生器(TRNG)硬件要求放大固有噪声和多比特熵输出,这在传统的单设备TRNG实现中难以实现。提出了一种双异质结噪声增强负跨导晶体管(BHN-NTC),该晶体管将不对称PTCDI-C13层纳入NTC晶体管中。该设计通过减小电子注入势垒(≈2.13 eV→≈0.41 eV)来增强电子注入,扩大NTC区(19→27 V),增加负跨导(VGS = -11 V时-0.036µS→VGS = -15 V时-0.073µS)。双异质结结构引入了噪声之间的强相关性,包括捕获/去捕获和生成/重组过程。该属性使TRNG中的熵吞吐量提高了三倍,每个采样事件实现3位输出。bhn - ntc驱动的TRNG利用增加的噪声诱导熵来生成更多样化的潜在向量,减轻模式崩溃,并能够合成高质量,逼真的图像。这显著增强了基于stylegan2的图像生成,提高了Frechet初始距离(FID)(18.7→8.3)、内核初始距离(KID)(0.024→0.009)、初始评分(IS)(6.5→9.2)和多尺度结构相似性(MS-SSIM)(0.43→0.21)等性能指标。因此,BHN-NTC晶体管建立了一个可扩展的随机噪声平台,推进了在安全电子和概率随机计算中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Heterojunction-Driven Stochasticity: Bi-Heterojunction Noise-Enhanced Negative Transconductance Transistor in Image Generation

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 VGS = −11 V → −0.073 µS at VGS = −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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来源期刊
Advanced Materials
Advanced Materials 工程技术-材料科学:综合
CiteScore
43.00
自引率
4.10%
发文量
2182
审稿时长
2 months
期刊介绍: Advanced Materials, one of the world's most prestigious journals and the foundation of the Advanced portfolio, is the home of choice for best-in-class materials science for more than 30 years. Following this fast-growing and interdisciplinary field, we are considering and publishing the most important discoveries on any and all materials from materials scientists, chemists, physicists, engineers as well as health and life scientists and bringing you the latest results and trends in modern materials-related research every week.
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