Repurposing Si CMOS nonidealities for stochastic and analog image processing

利用硅CMOS非理想特性进行随机和模拟图像处理

阅读:1

Abstract

Conventional semiconductor device engineering regards intrinsic device nonidealities as reliability concerns to be minimized or eliminated. Here, we demonstrate the strategic repurposing of these nonidealities as functional resources for advanced stochastic analog computing. We leverage two underutilized phenomena-deep-level channel trap-induced generation-recombination (G-R) noise and impact ionization-induced negative differential resistance (NDR) in body current-which have received limited attention compared to the extensively studied 1/f noise and monotonic drain current behavior in logic-centric transistors. By exploiting G-R noise with controllable temporal correlation and NDR with an unprecedented peak-to-valley ratio (2.78 × 10(4)) within fully depleted silicon-on-insulator transistors fabricated in industry silicon complementary metal-oxide semiconductor (CMOS) process, we achieve multifunctional analog computation at the single-device level. Our transistor seamlessly performs stochastic encryption, deterministic signal readout, and analog inversion simply through reconfiguration of applied bias conditions, thereby eliminating the need for peripheral random-number generators, dedicated analog inverters, or amplifiers. This approach not only reveals the previously unrecognized computational potential embedded in mature CMOS technologies but also presents a scalable and energy-efficient alternative to architecture based on exotic materials, laying the foundation for next-generation analog computing systems.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。