Kwon, Jong Ik Kim, Ji Su Seung, Hyojin Kim, Jihoon Cho, Hanguk Choi, Tae-Min Park, Jungwon Park, Juyoun Lim, Jung Ah Choi, Moon Kee Kim, Dae-Hyeong Choi, Changsoon 2025-05-22T05:30:16Z 2025-05-22T05:30:16Z 2025-05-21 2025-05 https://pubs.kist.re.kr/handle/201004/152451 Robotic vision has traditionally relied on high-performance yet resource-intensive computing solutions, which necessitate high-throughput data transmission from vision sensors to remote computing servers, sacrificing energy efficiency and processing speed. A promising solution is data compaction through contour extraction, visualizing only the outlines of objects while eliminating superfluous backgrounds. Here, we introduce an in-sensor multilevel image adjustment method using adjustable synaptic phototransistors, enabling the capture of well-defined images with optimal brightness and contrast suitable for achieving high-clarity contour extraction. This is enabled by emulating dopamine-mediated neuronal excitability regulation mechanisms. Electrostatic gating effect either facilitates or inhibits time-dependent photocurrent accumulation, adjusting photo-responses to varying lighting conditions. Through excitatory and inhibitory modes, the adjustable synaptic phototransistor enhances visibility of dim and bright regions, respectively, facilitating distinct contour extraction and high-accuracy semantic segmentation. Evaluations using road images demonstrate improvement of both object detection accuracy and intersection over union, and compression of data volume. English American Association for the Advancement of Science In-sensor multilevel image adjustment for high-clarity contour extraction using adjustable synaptic phototransistors Article 10.1126/sciadv.adt6527 1 Science Advances, v.11, no.18 Science Advances 11 18 Y scie scopus 001480343000026 2-s2.0-105004550125 Multidisciplinary Sciences Science & Technology - Other Topics Article TRANSMISSION MODULATION CORTEX ARRAY MACHINE VISION SEGMENTATION