Publication date: 15th April 2025
Artificial tactile synapses are neuromorphic devices that mimic the human skin's ability to perceive touch. They can detect subtle touches and convert pressure signals into electrical signals to stimulate the response of artificial synapses. These sensors can be trained to recognize different patterns of pressure, texture, and vibration. However, most current artificial tactile synapses are still mainly focused on rigid or flexible devices, limiting their applications in wearable electronics and intelligent sensing. Therefore, this study integrates the conjugated polymer PDBT-co-TT doped with ionic salts and the elastic polymer SBS as semiconductor layer materials to fabricate an artificial synapse transistor with synaptic plasticity. Simultaneously, it combines a pyramid-microstructured perovskite CsPbBr3 material with a polydimethylsiloxane (PDMS) thin film-based triboelectric nanogenerator (TENG). By converting the mechanical energy generated by pressure into electrical energy, it drives the artificial synapse transistor and utilizes a deep neural network (DNN) for Fashion MNIST image recognition.
The triboelectric nanogenerator prepared in this study demonstrates excellent performance in pressure sensing applications. It exhibits an output voltage as high as 89V under a pure pressure of 2.5 kPa, and achieves a sensitivity of 159.58 VkPa⁻¹ within a smaller pressure range. Furthermore, the device can effectively convert triboelectric charges into electrical energy to drive the artificial synapse transistor, thereby integrating a self-powered artificial synapse system. This system can exhibit the long-term potentiation (LTP) and long-term depression (LTD) effects of biological synapses, and utilizes these plasticity features as synaptic weights for a deep neural network (DNN) to further demonstrate the clothing image recognition capability on the Fashion MNIST dataset. This research not only achieves high-performance, low-power tactile sensing and artificial synapse simulation technology, but also provides a new technical pathway for the development of future intelligent sensing, self-powered neuromorphic computing, and bioelectronic fields.
NSTC-113-2124-M-027-001-
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