Zn2SnO4Thin Film Based Nonvolatile Positive Optoelectronic Memory for Neuromorphic Computing

Saransh Shrivastava, Yu Tang Lin, Bhaskar Pattanayak, Sparsh Pratik, Chia Cheng Hsu, Dayanand Kumar, Albert S. Lin, Tseung Yuen Tseng*


研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)


An optoelectronic synaptic device based on the ITO/Zn2SnO4(ZTO)/ITO structure is fabricated which integrates the electronic memory and optical sensing properties along with synaptic functions. The fabricated device shows over 80% optical transparency for the entire visible region (400-800 nm). Post-oxide annealing treatment is performed in a nitrogen environment at 200 °C. Significant improvements in bipolar resistive switching properties of the device with low SET voltage (+0.93 V) and long DC endurance cycles (∼12000) are observed in the annealed device. The linearity of such memristive synapse is improved for 350 training epochs with a total number of 175000 pulses. The spike time dependent plasticity learning rule for the annealed device is demonstrated through the electric field. The optical sensing capabilities of this device including photonic potentiation (responsivity: 0.52 μA/W), photonic paired pulse facilitation by adjusting time interval between two identical light pulses, learning experience behavior, and multilevel memory feature by the repetition of optical pulse for ∼103s are demonstrated under the blue light (wavelength "λ" = 405 nm) illumination at 50 mW/cm2. Photonic potentiation and electric depression behavior of the device mimic its nonvolatile synaptic plasticity. The linear fitting of I-V curve illustrates the dominance of Schottky emission and Poole-Frenkel conduction mechanisms at high and low resistance states, respectively. The electric response of the device is explained by the oxygen vacancy based filamentary model. The trapping and detrapping of electrons during the adsorption and desorption processes of atmospheric oxygen molecules on the ITO surface are responsible for the photoconduction phenomenon. To train the Hopfield neural network (HNN) model for image processing of 28 × 28 pixels, the normalized experimental data of long-term potentiation/depression are employed to mimic the learning behavior of the human brain. The convergence of electronic data storage and optical sensor has high potential which provides a path toward the future smart invisible optoelectronics for artificial intelligence.

頁(從 - 到)1784-1793
期刊ACS Applied Electronic Materials
出版狀態Published - 26 4月 2022


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