A scalable solution recipe for a Ag-based neuromorphic device

• Published in: Discover nano 2023-10, Vol.18 (1), p.124-124, Article 124
• Main Authors: Rao, Tejaswini S., Mondal, Indrajit, Bannur, Bharath, Kulkarni, Giridhar U.
• Format: Article
• Language: English
• Subjects: Associative learning; Behavior; Capacitance; Chemical process; Chemistry and Materials Science; cognition; Cognitive ability; Dewetting; Dilution; Drying; electric potential difference; Energy consumption; Glass substrates; Hierarchical structures; Long-term potentiation; Materials Science; Molecular Medicine; Nanochemistry; Nanoparticles; Nanoscale Science and Technology; Nanotechnology; Nanotechnology and Microengineering; Neuromorphic device; Precursors; Scanning electron microscopy; Self-forming; Short-term potentiation; Spin coating; Statistical analysis; Threshold voltage
• ISSN: 2731-9229; 1931-7573; 2731-9229; 1556-276X
• DOI: 10.1186/s11671-023-03906-5

Integration and scalability have posed significant problems in the advancement of brain-inspired intelligent systems. Here, we report a self-formed Ag device fabricated through a chemical dewetting process using an Ag organic precursor, which offers easy processing, scalability, and flexibility to address the above issues to a certain extent. The conditions of spin coating, precursor dilution, and use of solvents were varied to obtain different dewetted structures (broadly classified as bimodal and nearly unimodal). A microscopic study is performed to obtain insight into the dewetting mechanism. The electrical behavior of selected bimodal and nearly unimodal devices is related to the statistical analysis of their microscopic structures. A capacitance model is proposed to relate the threshold voltage (V th ) obtained electrically to the various microscopic parameters. Synaptic functionalities such as short-term potentiation (STP) and long-term potentiation (LTP) were emulated in a representative nearly unimodal and bimodal device, with the bimodal device showing a better performance. One of the cognitive behaviors, associative learning, was emulated in a bimodal device. Scalability is demonstrated by fabricating more than 1000 devices, with 96% exhibiting switching behavior. A flexible device is also fabricated, demonstrating synaptic functionalities (STP and LTP).