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Adv. Electron. Mater. 2022, 8, 2101124 DOI: 10.1002/aelm.202101124 There is an error in Figure 2 in the original published version of the article (https://doi.org/10.1002/aelm.202101124). One SEM (x = 0.035) was unintentionally zoomed from another image. Fortunately, none of the other results were affected by this unintentional error. The corrected version of Figure 2 is attached. Original figure:

The rapid rise of artificial intelligence (AI) is revolutionizing industries such as healthcare, robotics, and automation, creating a demand for more efficient, adaptive computing systems. In recognition of AI's profound impact, the 2024 Nobel Prize in Physics was awarded to John J. Hopfield and Geoffrey E. Hinton for their pioneering work in machine learning and artificial neural networks.[1-3] This

The first edition of the International Conference on Memristive Materials, Devices, and Systems (MEMRISYS) was held in Athens in April 2017. The aim was to consolidate at a single international conference on Memristor Technologies in the research communities in circuit theory, materials science, electronic devices, and neuromorphic computing. The 6th edition was held in Torino, Italy: while most of

Neuromorphic computing systems are promising alternatives in areas such as pattern recognition and image processing. This work focuses on the fabrication of tin oxide memristors (Ag/SnO2/Pt) to emulate artificial synapses and neurons. These tin oxide memristors demonstrate stable switching between threshold switch (TS) and resistive switch (RS) modes, achieved by adjusting the compliance current. Notably