TY - JOUR
T1 - On-receptor computing utilizing vertical-structured cost-effective memristor
AU - Ju, Dongyeol
AU - Lee, Subaek
AU - Lee, Jungwoo
AU - Kim, Sungjun
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/9/5
Y1 - 2024/9/5
N2 - A breakthrough in this field is the introduction of vertical resistive random-access memory (VRRAM), which features stacked electrodes capable of incorporating multiple devices in the same area, similar to the 3D NAND structure. Despite different fabrication processes, VRRAM retains the simple electrode–switching layer–electrode structure of traditional memristors, making it suitable for mimicking the synapse and neural connections in our brain, thus enhancing its adaptability to neuromorphic computing. Recent studies have aimed to advance functional computing systems, particularly in the realm of “on-receptor computing.” For this system adaptation, memristors need to exhibit sensor-perception behavior alongside traditional computing capabilities. One approach involves emulating the biological sensing process of the human body by establishing artificial nociceptors, which are dorsal root ganglion under the skin capable of sensing external stimuli. In this study, to achieve cost-effectiveness with a high storage density, a four-floor VRRAM is fabricated for implementing on-receptor computing. By applying controlled pulse streams to the VRRAM, the key nociceptive functions, including threshold, relaxation, no adaptation, and sensitization, are successfully demonstrated, highlighting the filament formation-based sensing properties of the memristor. Furthermore, by testing the reactance of the neuron to the applied action, namely, action-dependent synaptic plasticity, behaviors similar to those of biological neurons are demonstrated. Finally, by utilizing the multilevel properties of the VRRAM, a neural network-based pattern recognition system is constructed, showcasing the on-receptor computing capabilities of the VRRAM.
AB - A breakthrough in this field is the introduction of vertical resistive random-access memory (VRRAM), which features stacked electrodes capable of incorporating multiple devices in the same area, similar to the 3D NAND structure. Despite different fabrication processes, VRRAM retains the simple electrode–switching layer–electrode structure of traditional memristors, making it suitable for mimicking the synapse and neural connections in our brain, thus enhancing its adaptability to neuromorphic computing. Recent studies have aimed to advance functional computing systems, particularly in the realm of “on-receptor computing.” For this system adaptation, memristors need to exhibit sensor-perception behavior alongside traditional computing capabilities. One approach involves emulating the biological sensing process of the human body by establishing artificial nociceptors, which are dorsal root ganglion under the skin capable of sensing external stimuli. In this study, to achieve cost-effectiveness with a high storage density, a four-floor VRRAM is fabricated for implementing on-receptor computing. By applying controlled pulse streams to the VRRAM, the key nociceptive functions, including threshold, relaxation, no adaptation, and sensitization, are successfully demonstrated, highlighting the filament formation-based sensing properties of the memristor. Furthermore, by testing the reactance of the neuron to the applied action, namely, action-dependent synaptic plasticity, behaviors similar to those of biological neurons are demonstrated. Finally, by utilizing the multilevel properties of the VRRAM, a neural network-based pattern recognition system is constructed, showcasing the on-receptor computing capabilities of the VRRAM.
KW - Artificial neural network
KW - Artificial nociceptor
KW - On-receptor computing
KW - Vertical resistive random-access memory
UR - http://www.scopus.com/inward/record.url?scp=85194308068&partnerID=8YFLogxK
U2 - 10.1016/j.jallcom.2024.174926
DO - 10.1016/j.jallcom.2024.174926
M3 - Article
AN - SCOPUS:85194308068
SN - 0925-8388
VL - 998
JO - Journal of Alloys and Compounds
JF - Journal of Alloys and Compounds
M1 - 174926
ER -