Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Buried Contouring PTCDI-C13 Layer for Interface Engineering in Dual-Function Optical Synaptic and Memory Transistors

Authors
Kim, Yeo EunKang, SeungmeKim, HyeonjungKim, Young-JoonOh, SeyongYoo, Hocheon
Issue Date
Sep-2025
Publisher
AMER CHEMICAL SOC
Keywords
optical synaptic; memory transistor; PTCDI-C-13; neuromorphic computing; dual-function; surface roughness; contour layer
Citation
ACS APPLIED MATERIALS & INTERFACES
Indexed
SCIE
SCOPUS
Journal Title
ACS APPLIED MATERIALS & INTERFACES
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/126691
DOI
10.1021/acsami.5c14502
ISSN
1944-8244
1944-8252
Abstract
We present a heterojunction based on the n-type organic semiconductor N,N '-ditridecyl-3,4,9,10-perylenetetracarboxylic diimide (PTCDI-C-13) with a PTCDI-C-13/parylene/PTCDI-C-13-layered structure, enabling dual functionality as both an optical synaptic and a memory transistor. The device exploits a buried contouring PTCDI-C-13 layer, where the lower PTCDI-C-13 and intervening parylene layers serve distinct functions in charge trapping and modulation. In memory mode, the buried PTCDI-C-13 serves as a floating gate, while the parylene layer acts as a tunneling barrier, facilitating charge storage and controlled electron tunneling under combined optical and electrical stimulations. In synaptic mode, the thickness of the buried PTCDI-C-13 dictates the surface roughness, which is transferred to the parylene layer, forming a textured interface with abundant charge trap sites that modulate the synaptic behavior. By tuning the PTCDI-C-13 thickness, we controlled the interface roughness and trap density (n t), achieving optimal performance at 82 nm. The device successfully emulated synaptic plasticity and demonstrated transitions to long-term memory. To further verify its neuromorphic capabilities, our device achieved a recognition accuracy of 91.7% in a Modified National Institute of Standards and Technology-based classification simulation, successfully replicating biological synaptic behavior. Additionally, an electrocardiogram-based simulation demonstrated high classification accuracy while effectively processing dynamic, time-dependent signals. By reliably performing both static image recognition and dynamic biosignal processing, our device showcases its potential for real-time biomedical diagnostics, adaptive AI, and bioinspired computing applications.
Files in This Item
There are no files associated with this item.
Appears in
Collections
COLLEGE OF ENGINEERING SCIENCES > SCHOOL OF ELECTRICAL ENGINEERING > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher OH, SEYONG photo

OH, SEYONG
ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)
Read more

Altmetrics

Total Views & Downloads

BROWSE