Performance of in-materio physical reservoir computing devices based on highly oriented semiconducting polymer thin films
Abstract Physical reservoir computing (PRC) harnesses the intrinsic nonlinear dynamics of physical systems for efficient temporal data processing, offering significant advantages in energy-efficient hardware implementation. This study explores the potential of oriented semiconducting polymer (SCP) thin films as reservoirs for PRC, focusing on two types of SCP benzo[c]cinnoline-based conjugated polymer diketopyrrolopyrrole benzo[c]cinnoline p(DPP-BZC) and regioregular poly(3-hexyl thiophene) (RR-P3HT). To enable anisotropic charge transport, uniaxially oriented thin films with edge-on molecular orientation were fabricated using the floating film transfer method. The films were electrically evaluated for anisotropic nonlinear responses, phase-shifting capabilities, and high-dimensional mapping in PRC tasks. Performance metrics, including waveform generation accuracy, were systematically investigated under varying device configurations and molecular structures. The study underscores the critical role of different conjugated polymers and their orientations in PRC performance, paving the way for developing next-generation materials for temporal signal processing and low-power intelligent hardware.
Authors
- Shyam S. Pandey (ORCID: https://orcid.org/0000-0001-8102-1003)
- Yuya Ishizaki (ORCID: https://orcid.org/0000-0002-1243-0441)
- Yuki Usami (ORCID: https://orcid.org/0000-0002-8583-325X)
- Hirofumi Tanaka (ORCID: https://orcid.org/0000-0002-4378-5747)
- Shusaku Nagano (ORCID: https://orcid.org/0000-0002-3929-6377)
- Moulika Desu
- Ahmet Karacali (ORCID: https://orcid.org/0009-0000-4695-2814)
- Muzhen XU (ORCID: https://orcid.org/0009-0001-7103-4019)
Institutions
- Rikkyo University (JP)
- Kyushu Institute of Technology (JP)
Publication Details
- Journal
- Japanese Journal of Applied Physics
- Published
- 2025-03-12
- DOI
- https://doi.org/10.35848/1347-4065/adbf9f
- Citations
- 4
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 9.92