Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, China.
Department of Clinical Laboratory Medicine, TaiZhou Central Hospital (Taizhou University Hospital), Taizhou 318000, China.
ACS Appl Mater Interfaces. 2023 Oct 25;15(42):49390-49401. doi: 10.1021/acsami.3c09300. Epub 2023 Oct 10.
Memristor synapses based on green and pollution-free organic materials are expected to facilitate biorealistic neuromorphic computing and to be an important step toward the next generation of green electronics. Metalloporphyrin is an organic compound that widely exists in nature with good biocompatibility and stable chemical properties, and has already been used to fabricate memristors. However, the application of metalloporphyrin-based memristors as synaptic devices still faces challenges, such as realizing a high switching ratio, low power consumption, and bidirectional conductance modulation. We developed a memristor that improves the resistive switching (RS) characteristics of Zn(II)meso-tetra(4-carboxyphenyl) porphine (ZnTCPP) by combining it with deoxyribonucleic acid (DNA) in a composite film. The as-fabricated ZnTCPP-DNA-based device showed excellent RS memory characteristics with a sufficiently high switching ratio of up to ∼10, super low power consumption of ∼39.56 nW, good cycling stability, and data retention capability. Moreover, bidirectional conductance modulation of the ZnTCPP-DNA-based device can be controlled by modulating the amplitudes, durations, and intervals of positive and negative pulses. The ZnTCPP-DNA-based device was used to successfully simulate a series of synaptic functions including long-term potentiation, long-term depression, spike time-dependent plasticity, paired-pulse facilitation, excitatory postsynaptic current, and human learning behavior, which demonstrates its potential applicability to neuromorphic devices. A two-layer artificial neural network was used to demonstrate the digit recognition ability of the ZnTCPP-DNA-based device, which reached 97.22% after 100 training iterations. These results create a new avenue for the research and development of green electronics and have major implications for green low-power neuromorphic computing in the future.
基于绿色环保有机材料的忆阻器突触有望促进生物拟态神经形态计算，并朝着下一代绿色电子产品迈出重要一步。金属卟啉是一种广泛存在于自然界中的有机化合物，具有良好的生物相容性和稳定的化学性质，已经被用于制造忆阻器。然而，基于金属卟啉的忆阻器作为突触器件的应用仍然面临挑战，例如实现高开关比、低功耗和双向电导调制。我们通过将脱氧核糖核酸（DNA）与锌(II)meso-四(4-羧基苯基)卟啉（ZnTCPP）复合薄膜相结合，开发了一种改善 ZnTCPP 电阻开关（RS）特性的忆阻器。所制备的 ZnTCPP-DNA 基器件表现出优异的 RS 记忆特性，具有高达约 10 的高开关比、低至约 39.56 nW 的超低功耗、良好的循环稳定性和数据保持能力。此外，通过调制正、负脉冲的幅度、持续时间和间隔，可以控制 ZnTCPP-DNA 基器件的双向电导调制。ZnTCPP-DNA 基器件成功模拟了一系列突触功能，包括长时程增强、长时程抑制、尖峰时间依赖性可塑性、成对脉冲易化、兴奋性突触后电流和人类学习行为，这表明其在神经形态器件中有潜在的应用前景。使用两层人工神经网络来演示 ZnTCPP-DNA 基器件的数字识别能力，经过 100 次训练迭代后，其识别能力达到 97.22%。这些结果为绿色电子产品的研究和开发开辟了新途径，对未来绿色低功耗神经形态计算具有重要意义。
