DPP-DTT Nanowire Phototransistors for Optoelectronic Synapses in EMG and ECG Signal Classification

Area of Science:

• Organic electronics
• Neuromorphic computing
• Nanotechnology

Background:

• Neuromorphic computing aims to mimic the human brain's structure and function.
• Organic semiconductors offer potential for low-cost, flexible electronic devices.
• Phototransistors can be utilized as artificial synapses for neuromorphic applications.

Purpose of the Study:

• To develop and characterize a neuromorphic phototransistor based on nanowire-patterned diketopyrrolo-pyrrole-dithienylthieno[3,2-b]thiophene (DPP-DTT).
• To investigate the optically stimulated synaptic behavior and plasticity of the device.
• To evaluate the device's performance in artificial intelligence classification tasks.

Main Methods:

• Fabrication of well-aligned DPP-DTT nanowires using soft lithography.
• Characterization of photogating effect and threshold voltage shifts under blue illumination.
• Emulation of synaptic potentiation and depression using light and gate pulses.
• Modulation of synaptic plasticity by varying stimulus parameters.
• Performance evaluation in image recognition and physiological signal classification.

Main Results:

• The DPP-DTT phototransistor exhibits optically stimulated synaptic behavior with efficient trap-detrapping dynamics.
• Threshold voltage shifts up to 6.4 V were observed due to electron trapping and detrapping.
• Synaptic plasticity was successfully modulated, enabling transitions from short- to long-term memory.
• High classification accuracies were achieved: 97.4% for MNIST, 93.4% for EMG, 89.0% for ECG, and 83.8% for CIFAR-10.

Conclusions:

• The developed neuromorphic phototransistor shows promise for advanced artificial intelligence applications.
• The nanowire geometry facilitates efficient synaptic plasticity emulation.
• This work contributes to the development of next-generation organic neuromorphic devices.