Polarity‐Programmable Response in Bipolar‐Barrier Heterojunctions for Reconfigurable Detection
Shian Mi, Xuhao Fan, Fengyi Zhu, Sheng Ni, 茹文君, Yuhang Ma, Xiaohan Wei, Chongyu Li 等 18 位
China Jiliang University Northwestern Polytechnical University Shanghai Institute of Technical Physics
阅读操作
确认中在文库中上传 PDF 后可生成中文音频讲解。
摘要与影响
Next‐generation intelligent vision systems demand not only high‐fidelity sensing but also in‐sensor processing that is fast, adaptive, and energy‐efficient. Existing in‐sensor paradigms ignore photocurrent polarity—an intrinsic decision variable—forcing computation into amplitude thresholds or multi‐device pipelines. Here, we present a polarity‐programmable optoelectronic device based on bipolar‐barrier heterostructures equipped separately with vertical and horizontal channels. By exploiting the interplay between bipolar barriers, the device enables modulation of photocurrent polarity—realizing dynamic ternary‐state output (‘−1’, ‘0’, ‘+1’) governed solely by laser position, bias voltage, wavelength, and power‐ coded. The vertical structure exhibits a bipolar response with an ultralow switching bias of −0.025 V (less than 1/10 that of the horizontal structure), along with wavelength‐tunable polarity, a responsivity of 0.22 A/W, a detectivity of 8.6 × 10 9 cm Hz 1/2 W −1 , and a fast response time of ∼ 121 µs due to its large photosensitive area, shortened carrier transport, and wavelength‐dependent optical field distribution. Programming wavelength and power density enable a single device to deterministically realize all Boolean gates, polarity‐resolved imaging, and visible‐infrared dual‐channel transmission. This multidimensional polarity‐programmable pathway in bipolar‐barrier heterojunctions intrinsically integrates sensing, signal encoding, and logic operations within a single physical unit, establishing polarity programming as a scalable paradigm for optoelectronic computing.
逐年被引趋势
关键指标
同类平均 = 1
同领域 · 同年份 · 同类型
Google Scholar 与 OpenAlex 的被引统计范围不同,数值存在差异属正常。
AI 辅助阅读
依据:摘要
可就本文提问;依据不足时会说明。
学术脉络
学科主题
计算机 / AINeural Networks and Reservoir Computing
Transition Metal Oxide Nanomaterials · Advanced Memory and Neural Computing
参考文献 27
此处列出前 3 条
引用本文 2
按被引量排序,此处列出前 3 条