Optoelectronic‐Driven Multi‐Functional MoS 2 :PVA Flexible Memristor for In‐Sensor Neuromorphic Computing
ABSTRACT This article presents a multifunctional Ag/MoS 2 :PVA/SnO 2 /Pt memristive device that demonstrates resistive memory, synaptic learning, and optical information processing. The device exhibited stable bipolar resistive switching with low operating voltages with SET and RESET at 0.57/−0.86 V, respectively, an ON/OFF ratio of 400 at 0.2 V, endurance around cycles, and retention stability over s with resistance separation greater than . The device also maintained reliable operation under repeated bending and different bending radii, confirming excellent mechanical flexibility. Gradual conductance modulation enabled repeatable potentiation and depression behavior for neuromorphic functionality. Under optical stimulation, wavelength‐ and intensity‐dependent conduction evolution was observed and followed an exponential learning model, enabling optical memory characteristics. Furthermore, image memorization and traffic signal recognition were demonstrated using experimentally extracted conductance reconstruction. A comprehensive conduction switching model based on ion migration, conductive pathway modulation, and photo‐induced carrier modulation is proposed to explain the multifunctional response. These results demonstrate the potential of the proposed platform for artificial vision and in‐sensor neuromorphic computing.
Authors
- Parthasarathi Pal (ORCID: https://orcid.org/0000-0002-4888-9596)
- Saurabh Kumar Pandey (ORCID: https://orcid.org/0000-0002-5013-6745)
- Harsh Ranjan (ORCID: https://orcid.org/0009-0003-1254-4005)
- Sanjay Kumar (ORCID: https://orcid.org/0000-0001-5382-2006)
- Gulshan Kumar (ORCID: https://orcid.org/0009-0006-5583-2375)
Institutions
- Indian Institute of Technology Patna (IN)
- SRM University, Andhra Pradesh (IN)
- SRM University (IN)
- Liverpool John Moores University (GB)
Publication Details
- Journal
- Small
- Published
- 2026-10-16
- DOI
- https://doi.org/10.1002/smll.76190
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00