Date Time Researchers Present Spontaneous Sparse Learning for PCM-based Memristor Neural Networks An international team of researchers, affiliated with UNIST has unveiled a novel technology that could improve the learning ability of artificial neural networks (ANNs). Professor Hong-Sik Chungs and his research team in the Department of Materials Science and Engineering at UNIST, in collaboration with researchers from Tsinghua University in China, proposed a new learning method to improve the learning ability of ANN chips by challenging its instability. Artificial neural network chips are capable of mimicking the structural, functional and biological features of human neural networks, and thus have been considered the technology of the future. In this study, the research team demonstrated the effectiveness of the proposed learning method by building phase change memory (PCM) memristor arrays that operate like ANNs. This learning method also has the advantage that learning ability can be improved without additional power consumption because it uses the spontaneous increase in electrical resistance of the information storage material (phase change material).