Interface Engineering Enables Multilevel Resistive Switching in Ultra-Low-Power Chemobrionic Copper Silicate

Area of Science:

• Materials Science
• Nanotechnology
• Electronics

Background:

• Memristors are crucial for advanced technologies like artificial intelligence (AI) and the Internet of Things (IoT) due to their low power consumption and parallel processing capabilities, addressing von Neumann architecture limitations.
• Chemobrionics, or chemical gardens, offer a novel approach to fabricating advanced electronic components.
• Copper silicate-based materials present an opportunity for developing efficient memristive devices.

Purpose of the Study:

• To demonstrate resistive switching in copper silicate-based hollow tube-forming self-organized membranes for memristor applications.
• To showcase the potential of chemobrionics for cost-effective and highly efficient memristor fabrication.
• To evaluate the synaptic characteristics of these memristors for neuromorphic computing.

Main Methods:

• Fabrication of a memristor device with the architecture ITO/PEDOT:PSS/copper silicate/PMMA/Ag.
• Characterization of current-voltage (I-V) hysteresis, SET voltage, power consumption, and data endurance.
• Assessment of self-rectifying behavior and synaptic characteristics like paired-pulse facilitation (PPF) and potentiation and depression (P&D).

Main Results:

• The copper silicate memristor exhibited stable current-voltage hysteresis with a low SET voltage of ~0.2 V and low power consumption of 0.8 nJ.
• The device demonstrated robust data endurance and multilevel resistive switching capabilities.
• A high rectification ratio of 60 was observed, indicating significant potential for electronic applications.
• The memristor successfully emulated biological synaptic functions, including PPF and P&D.

Conclusions:

• Copper silicate chemical garden-based memristors offer a cost-effective and efficient solution for advanced electronic applications.
• These devices show promise for real-time synaptic processing required in neuromorphic computing and AI.
• The solution-based, low-temperature fabrication process conducted under ambient conditions simplifies manufacturing and reduces costs.