Region-dependent artificial pain perception based on synaptic transistors mimicking differential nociceptor sensitivity

Nociceptors, which detect pain against harmful stimuli, exhibit region-dependent sensitivity, leading to variations in perceived pain intensity across different parts of the body. Herein, we propose that region-dependent nociceptive responses emulate by reflecting differences in the number of activated fibers through low-pain-mimicking synaptic transistor (LST) and high-pain-mimicking synaptic transistor (HST), each characterized by distinct synaptic weights. The variation in the synaptic weight is achieved by modulating the charge-trapping capability of high-k HfO_X, which is precisely controlled by the ultraviolet treatment time. Under 64 consecutive spike inputs, the LST and HST exhibited average post-synaptic current variations of 134.0% and 261.0%, respectively, across device-to-device measurements. The back of the hand and the calf, which exhibit different pressure pain thresholds, were taken as representative examples to demonstrate that the perceived pain intensity differs across body regions in response to identical stimulus magnitudes. These results validate their capability to emulate region-dependent variations in pain perception across body regions. Furthermore, these results demonstrate that key nociceptive characteristics such as threshold behavior, no adaptation, and sensitization exhibit different firing points depending on the magnitude of the applied stimulus, and that the onset of these characteristics also varies with stimulus frequency. Furthermore, to more realistically emulate the nociceptive characteristics of the human body, a region-dependent artificial pain perception system was developed by integrating a force-sensitive resistor sensor, an Arduino-based signal processor, and synaptic transistors. Through this region-dependent artificial pain perception system, realistic pain-like sensory feedback was achieved while emulating sensitization phenomena such as allodynia and hyperalgesia, demonstrating a hardware-level implementation that reproduces region-dependent pain transmission observed in the human body and offers significant potential for neuroprosthetics, robotics, and e-skin applications.