A self-adaptive hardware with resistive switching synapses for experience-based neurocomputing.

Neurobiological systems continually interact with the surrounding environment to refine their behaviour toward the best possible reward. Achieving such learning by experience is one of the main challenges of artificial intelligence, but currently it is hindered by the lack of hardware capable of plastic adaptation. Here, we propose a bio-inspired recurrent neural network, mastered by a digital system on chip with resistive-switching synaptic arrays of memory devices, which exploits homeostatic Hebbian learning for improved efficiency.

One-step regression and classification with cross-point resistive memory arrays.

Machine learning has been getting attention in recent years as a tool to process big data generated by the ubiquitous sensors used in daily life. High-speed, low-energy computing machines are in demand to enable real-time artificial intelligence processing of such data. These requirements challenge the current metal-oxide-semiconductor technology, which is limited by Moore's law approaching its end and the communication bottleneck in conventional computing architecture.

Solving matrix equations in one step with cross-point resistive arrays.

Conventional digital computers can execute advanced operations by a sequence of elementary Boolean functions of 2 or more bits. As a result, complicated tasks such as solving a linear system or solving a differential equation require a large number of computing steps and an extensive use of memory units to store individual bits. To accelerate the execution of such advanced tasks, in-memory computing with resistive memories provides a promising avenue, thanks to analog data storage and physical computation in the memory.

Surface diffusion-limited lifetime of silver and copper nanofilaments in resistive switching devices.

Silver/copper-filament-based resistive switching memory relies on the formation and disruption of a metallic conductive filament (CF) with relatively large surface-to-volume ratio. The nanoscale CF can spontaneously break after formation, with a lifetime ranging from few microseconds to several months, or even years. Controlling and predicting the CF lifetime enables device engineering for a wide range of applications, such as non-volatile memory for data storage, tunable short/long term memory for synaptic neuromorphic computing, and fast selection devices for crosspoint arrays.

Impact of oxide and electrode materials on the switching characteristics of oxide ReRAM devices.

Resistive switching random-access memory (ReRAM) is one of the most promising technologies for non-volatile memories. Thanks to the low power and high speed operation, the high density CMOS-compatible integration, and the high cycling endurance, the ReRAM technology is becoming a strong candidate for high-density storage arrays and novel in-memory computing systems. However, ReRAM suffers from cycle-to-cycle switching variability and noise-induced resistance fluctuations, leading to insufficient read margin between the programmed resistive states.