Search

One long-standing goal in the emerging neuromorphic field is to create a reliable neural network hardware implementation that has low energy consumption, while providing massively parallel computation. Although diverse oxide-based devices have made significant progress as artificial synaptic and neuronal components, these devices still need further optimization regarding linearity, symmetry, and stability. Here, we present a proof-of-concept experiment for integrated neuromorphic computing networks by utilizing spintronics-based synapse (spin-S) and neuron (spin-N) devices, along with linear and symmetric weight responses for spin-S using a stripe domain and activation functions for spin-N. An integrated neural network of electrically connected spin-S and spin-N successfully proves the integration function for a simple pattern classification task. We simulate a spin-N network using the extracted device characteristics and demonstrate a high classification accuracy (over 93%) for the spin-S and spin-N optimization without the assistance of additional software or circuits required in previous reports. These experimental studies provide a new path toward establishing more compact and efficient neural network systems with optimized multifunctional spintronic devices. Neuromorphic computing: A new spin on synapses and neurons An array of low-power devices that mimic the behavior of the brain has been constructed by researchers in South Korea. The brain works by passing chemical and electrical signals across a network of cells called neurons, which are connected via synapses. Scientists want to create an artificial neural network to take advantage of the parallel way in which the brain processes information. YeonJoo Jeong from the Korea Institute of Science and Technology, Jin Pyo Hong from Hanyang University, both in Seoul, and their colleagues have demonstrated a proof-of-principle neuromorphic computing network using so-called spintronic devices. Spintronics are low-power devices that use the property of an electron called spin rather than its charge as in conventional electronics. The team's neural network of electrically connected spin-synapses and spin-neurons successfully performed a simple pattern classification task. We introduced spintronics-based synapses (spin-S) by utilizing a stripe domain ensuring its highly linear and symmetric weight responses, together with domain-wall motion-based neurons (spin-N) for activation functions. In addition, a crossbar array architecture for the spin-S/N has been proposed and experimentally demonstrated. A simple pattern-classification task was tested using an integrated network of electrically connected spin-S and spin-N to mimic a human brain. Our experimental findings provide a new avenue toward establishing more efficient neural network systems with spintronic devices. [ABSTRACT FROM AUTHOR]

Conductive bridging random access memory (CBRAM) has been considered to be a promising emerging device for artificial synapses in neuromorphic computing systems. Good analog synaptic behaviors, such as linear and symmetric synapse updates, are desirable to provide high learning accuracy. Although numerous efforts have been made to develop analog CBRAM for years, the stochastic and abrupt formation of conductive filaments hinders its adoption. In this study, we propose a novel approach to enhance the synaptic behavior of a SiNx/a-Si bilayer memristor through Ge implantation. The SiNx and a-Si layers serve as switching and internal current limiting layers, respectively. Ge implantation induces structural defects in the bulk and surface regions of the a-Si layer, enabling spatially uniform Ag migration and nanocluster formation in the upper SiNx layer and increasing the conductance of the a-Si layer. As a result, the analog synaptic behavior of the SiNx/a-Si bilayer memristor, such as the nonlinearity, on/off ratio, and retention time, is remarkably improved. An artificial neural network simulation shows that the neuromorphic system with the implanted SiNx/a-Si memristor provides a 91.3% learning accuracy mainly due to the improved linearity. Memory devices: Ionic implants help mimic the synapse An approach that tweaks the structures of a new type of memory chip shows promise for neuromorphic computing applications. In conductive bridging random access memory (CBRAM) technology, nanoscale metal filaments inside special insulators are used to store data in either high or low conductance states. Inho Kim from the Korea Institute of Science and Technology in Seoul, South Korea and colleagues now report beneficial effects when germanium ions are implanted into silicon-based CBRAM using high-energy beams. The team's microscopy experiments revealed that the implantation caused metal nanoclusters to distribute evenly within the device's switching layer. These particles enabled smoother transitions between conductance states than conventional CBRAM, better mimicking the analog switching seen in synapses. Neural network simulations demonstrated that ion-implanted devices had significantly improved learning accuracy compared to unmodified CBRAM chips. We propose a novel approach to enhance the synaptic behavior of a SiNx/a-Si bilayer memristor through Ge implantation. The SiNx and a-Si layers serve as switching and internal current limiting layers, respectively. Ge implantation induces structural defects in the bulk and surface regions of the a-Si layer, enabling spatially uniform Ag migration and nanocluster formation in the upper SiNx layer and increasing the conductance of the a-Si layer. As a result, the analog synaptic behavior of the SiNx/a-Si bilayer memristor, such as the nonlinearity, on/off ratio, and retention time, is remarkably improved. [ABSTRACT FROM AUTHOR]