Focus issue on hafnium oxide based neuromorphic devices

Our pathway towards intelligent computing systems leads to an ever-increasing amount of data movement, which apparently pushes the conventional von-Neumann architecture towards its limits in terms of performance and energy consumption. Combining computing and storage functionality locally within one structure is seen as one potential branch to bypass this issue. Neural Networks are a very promising path in that direction. Artificial neural networks rely to a large extend on “synaptic weight” storage and multiply-accumulate (MAC) functionality in either digital or analogue way, the latter one making use of Kirchhoff’s and Ohm’s law. The brain inspired neuromorphic computing architectures go one step further and more directly mimic the key biological elements of the brain: neurons and synapses. For hardware realization of such neuromorphic computing architectures the availability of scalable non-volatile memory devices to realize high density synapses for deep learning artificial neural networks or brain inspired spiking neural networks is essential. This NCE Focus Issue concentrates on the discussion of hafnium oxide based neuromorphic devices. Hafnium oxide has become a standard dielectric material in complementary metal oxide semiconductor (CMOS) fabrication processes since its introduction as gate dielectric for metal oxide semiconductor field effect Transistors (MOSFETs) back in 2007. Since then, its possible application field has significantly widened into the usage as memory devices. It was shown that valence change based resistive switching devices, better known as either resistive random-access memory (RRAM) or Memristor, could be realized with good properties using hafnium oxide. Moreover, in 2011 it was reported that under special conditions hafnium oxide can even be transformed into a ferroelectric. The latter enables a variety of different types of memory cells, namely capacitor based ferroelectric random access memories (FeRAM), ferroelectric field effect transistors (FeFET) and ferroelectric tunneling junctions (FTJ). This special issue will cover all aspects of using hafnium oxide based devices in neuromorphic systems starting from the material optimization via device concepts and modeling towards simulation and integration of neuromorphic systems.