Black Phosphorus/Ferroelectric P(VDF-TrFE) Field-Effect Transistors with High Mobility for Energy-Efficient Artificial Synapse in High-Accuracy Neuromorphic Computing

Abstract

The neuromorphic system is an attractive platform for next-generation computing with low power and fast speed to emulate knowledge-based learning. Here, we design ferroelectrictuned synaptic transistors by integrating 2D black phosphorus (BP) with a flexible ferroelectric copolymer poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)). Through nonvolatile ferroelectric polarization, the P(VDF-TrFE)/BP synaptic transistors show a high mobility value of 900 cm 2 V −1 s −1 with a 10 3 on/ off current ratio and can operate with low energy consumption down to the femtojoule level (∼40 fJ). Reliable and programmable synaptic behaviors have been demonstrated, including paired-pulse facilitation, long-term depression, and potentiation. The biological memory consolidation process is emulated through ferroelectric gate-sensitive neuromorphic behaviors. Inspiringly, the artificial neural network is simulated for handwritten digit recognition, achieving a high recognition accuracy of 93.6%. These findings highlight the prospects of 2D ferroelectric field-effect transistors as ideal building blocks for high-performance neuromorphic networks.

Polarized Tunneling Transistor for Ultralow-Energy-Consumption Artificial Synapse toward Neuromorphic Computing

“…The corresponding highest recognition accuracy achieved is 95%, which surpasses the performance of most synaptic devices based on three-terminal transistors (Table ). ,,,,,− Moreover, the corresponding mapping-synaptic-weights image (Figure S14d) is distinguishable and closely resembles the pristine image (Figure S14c). We developed a video (Supplementary Video 2) that demonstrates the progressive improvement in the clarity of the face outline, confirming the potential applications of our synaptic device in high-energy-efficient neuromorphic computing systems.…”

Ultrasoft and High-Adhesion Block Copolymers for Neuromorphic Computing

“…Through a series of 100 negative spikes (−1.8 V) and 100 positive spikes (+1.0 V), TPOAS demonstrated symmetric potentiation/depression states (Figure A). This result enabled the simulation of ANNs designed to recognize Modified National Institute of Standards and Technology database (MNIST) handwritten digit data using backpropagation techniques . In the simulation, 28 × 28 MNIST data were unwrapped into 1 × 784 row vectors to facilitate vector matrix multiplication.…”

PZT-Enabled MoS2 Floating Gate Transistors: Overcoming Boltzmann Tyranny and Achieving Ultralow Energy Consumption for High-Accuracy Neuromorphic Computing

“…As depicted in Figure h, after training for 150 learning epochs, the recognition accuracy of our ANN reaches 97.3%, which is slightly lower than the recognition accuracy of 98.5% achieved using the ideal software weight updates. Our synaptic device outperforms most synaptic devices based on three-terminal transistors (Table ) ,,,,− in terms of recognition accuracy and weight-updating energy consumption. As shown in panels i–k of Figure , the synaptic weight mapping image (Figure j) of the ideal MLP simulator closely resembles the input pristine image (Figure i).…”