Electroforming-Free and Non-Volatile Fused Polydithienothiophene-Based Organic Memory Devices: Effect of Film Thickness on Performance
The pursuit of advanced materials for resistive random-access memory (RRAM) has created significant interest in organic semiconductors due to their advantageous properties, including tunable electrical characteristics, flexibility, and low-fabrication cost. While resistive switching behavior has been well-studied in polymers like poly(3,4-ethylenedioxythiophene) (PEDOT), this study represents the first investigation of fused polydithienothiophene (PDTT) in this context. This work reports PDTT as a promising material for RRAM devices, with the device architecture as ITO/PDTT/Al. The structural, optical, morphological, and electrochemical characteristics of PDTT thin films were confirmed by FTIR, UV–visible absorption spectroscopy, FESEM, and cyclic voltammetry. The ITO/PDTT/Al devices exhibit forming-free, nonvolatile, and bipolar resistive switching, with a SET voltage of −1.0 V and a RESET voltage of +1.0 V. Device A, B, and C with varying PDTT layer thicknesses were fabricated to investigate the impact on resistive switching performance, device stability, and cycling endurance. Device A, with 41 nm PDTT layer demonstrates stable performance for 110 cycles with low switching voltages and good reproducibility, making it promising candidate for scalable, nonvolatile memory applications. Additionally, the compatibility of fused PDTT with ITO substrates, coupled with the simple device fabrication approach, enhances its potential for seamless integration into modern electronic devices.
Authors
- Ajeet Kumar (ORCID: https://orcid.org/0000-0002-4878-505X)
- Asit Patra (ORCID: https://orcid.org/0000-0002-3492-9147)
- Rashi Kedia (ORCID: https://orcid.org/0000-0003-4688-6380)
- Ritu Rohilla
Institutions
- Council of Scientific and Industrial Research (IN)
- Academy of Scientific and Innovative Research (IN)
Publication Details
- Journal
- ACS Applied Polymer Materials
- Published
- 2025-11-29
- DOI
- https://doi.org/10.1021/acsapm.5c03673
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73