Organic Ferroelectric Synaptic Transistors for Neural Image Recognition Networks
Abstract Combined with polymer ferroelectric dielectrics, organic field‐effect transistors are promising candidates for both electrical and photonic synapses to emulate important functions of biological synapses. In this work two distinct copolymers of poly (vinylidene fluoride) (PVDF) with trifluor...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley-VCH
2025-07-01
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| Series: | Advanced Materials Interfaces |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/admi.202401035 |
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| Summary: | Abstract Combined with polymer ferroelectric dielectrics, organic field‐effect transistors are promising candidates for both electrical and photonic synapses to emulate important functions of biological synapses. In this work two distinct copolymers of poly (vinylidene fluoride) (PVDF) with trifluoroethylene and hexafluoropropylene, PVDF‐TrFE and PVDF‐HFP, respectively, are utilized as ferroelectric dielectrics due to their polarization control and non‐volatile polarization hysteresis. Using a donor–acceptor copolymer as the semiconductor layer, bottom‐gate, top‐contact transistors are fabricated with externally poled and unpoled films of PVDF‐TrFE and PVDF‐HFP where the operating voltages are less than 10 V. On average, poled PVDF‐TrFE FETs show improved characteristics with carrier mobilities > 1 cm2 V−1 s−1. The individual transistors are evaluated in a system level network for image recognition. The synaptic response of these devices is quantified using key metrics such as the dynamic range and nonlinearity of the analog channel conductance modulation, which are then employed to simulate the neural network behavior. The accuracy of the network in recognizing a set of handwritten digits is used to assess the effectiveness of these devices in neuromorphic architectures. The results are analyzed in terms of the poling condition of the ferroelectric dielectric, the margin of conductance modulation, and the nonlinear weight updates. |
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| ISSN: | 2196-7350 |