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Abstract For hardware artificial intelligence, the central task is to design and develop artificial synapses with needed characteristics. Here, the design and experimental demonstration of a van der Waals (vdW) photo‐ferroelectric synapse are reported. In the photo‐ferroelectric synapse, the synaptic memory is extracted by reading the photocurrent, and written or edited by electrical pulses. The semiconducting vdW organic‐inorganic halide perovskite ((R)‐(–)‐1‐cyclohexylethylammonium)PbI3(R‐CYHEAPbI3) photo‐ferroelectric serves as the model photo‐ferroelectric channel. Here, the vdW organic layer provides ferroelectric dipole and the PbI6octahedron is responsible for photon absorption and charge transport. The R‐CYHEAPbI3photo‐ferroelectric synapse show a writing/reading dynamics with >200 synaptic states, close to 103on/off ratio, and reasonable endurance and retention characteristics. With the experimentally measured weight dynamics (parallel reading through ferroelectric photovoltaic effect and writing by electrical pulses) of R‐CYHEAPbI3synapses, the feasibility of using a crossbar circuit to implement classic training and inference of hand‐written digits is presented. An image recognition accuracy of up to 90% is obtained. The demonstration of such a vdW photo‐ferroelectric synapse opens a window in the design of advanced devices for artificial intelligence.
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This content will become publicly available on February 1, 2026
Enhanced Synaptic Memory Window and Linearity in Planar In 2 Se 3 Ferroelectric Junctions
Abstract A synaptic memristor using 2D ferroelectric junctions is a promising candidate for future neuromorphic computing with ultra‐low power consumption, parallel computing, and adaptive scalable computing technologies. However, its utilization is restricted due to the limited operational voltage memory window and low on/off current (ION/OFF) ratio of the memristor devices. Here, it is demonstrated that synaptic operations of 2D In2Se3ferroelectric junctions in a planar memristor architecture can reach a voltage memory window as high as 16 V (±8 V) and ION/OFFratio of 108, significantly higher than the current literature values. The power consumption is 10−5 W at the on state, demonstrating low power usage while maintaining a large ION/OFFratio of 108compared to other ferroelectric devices. Moreover, the developed ferroelectric junction mimicked synaptic plasticity through pulses in the pre‐synapse. The nonlinearity factors are obtained 1.25 for LTP, −0.25 for LTD, respectively. The single‐layer perceptron (SLP) and convolutional neural network (CNN) on‐chip training results in an accuracy of up to 90%, compared to the 91% in an ideal synapse device. Furthermore, the incorporation of a 3 nm thick SiO2interface between the α‐In2Se3and the Au electrode resulted in ultrahigh performance among other 2D ferroelectric junction devices to date.
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- Award ID(s):
- 2118806
- PAR ID:
- 10637779
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Advanced Materials
- Volume:
- 37
- Issue:
- 6
- ISSN:
- 0935-9648
- Page Range / eLocation ID:
- 2413178
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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