Physics

Neuromorphic engineering

Nanotechnology

Memristor

Engineering

Artificial intelligence

Mesoscopic physics

Materials science

Electronic engineering

Computer science

Quantum mechanics

Flexible electronics

Optoelectronics

Artificial neural network

ACS Applied Electronic Materials

The growth of flexible memory devices with intrinsic neuromorphic properties is attracting tremendous attention due to the wide spread of wearable electronics. The fabrication of robust artificial synaptic structures that will have the ability to remain operational after various strain loads and perform artificial synaptic weight modulation procedures is considered the cornerstone for the development of wearable neuromorphic computing systems. Along these lines, a low-temperature Ag/SiO2/TiN memristive device is demonstrated on a polyethylene naphthalate substrate decorated with Pt nanoparticles (NPs) with a 5 nm average diameter. The memory devices are completely forming-free, operate at a low voltage of 500 mV, and exhibit great stability after bending numerous times under a record high mechanical strain of 4.16% without any severe degradation of the pulse endurance and retention capabilities. Concrete pieces of evidence regarding the crack suppression effect induced by the layer of Pt NPs on the robust memory performance are also provided. Moreover, a thorough analysis of the neuromorphic properties of our prototypes is presented, including the implementation of short-term plasticity effects, such as paired-pulsed facilitation and paired-pulse depression, which are of great importance for the rehearsal procedures of neurons during the learning function as well as for the linear distribution of the conductance pattern during synaptic potentiation and depression tasks. Additionally, Hebbian spike-time-dependent-plasticity responses were also recorded.

Highly Flexible Artificial Synapses from SiO<sub>2</sub>-Based Conductive Bridge Memristors and Pt Nanoparticles through a Crack Suppression Technique

Highly Flexible Artificial Synapses from SiO2-Based Conductive Bridge Memristors and Pt Nanoparticles through a Crack Suppression Technique

Highly Flexible Artificial Synapses from SiO₂-Based Conductive Bridge Memristors and Pt Nanoparticles through a Crack Suppression Technique