Hardware implementation of a true random number generator integrating a hexagonal boron nitride memristor with a commercial microcontroller.

The development of the internet-of-things requires cheap, light, small and reliable true random number generator (TRNG) circuits to encrypt the data-generated by objects or humans-before transmitting them. However, all current solutions consume too much power and require a relatively large battery, hindering the integration of TRNG circuits on most objects. Here we fabricated a TRNG circuit by exploiting stable random telegraph noise (RTN) current signals produced by memristors made of two-dimensional (2D) multi-layered hexagonal boron nitride (h-BN) grown by chemical vapor deposition and coupled with inkjet-printed Ag electrodes.

A Hybrid CMOS-Memristor Spiking Neural Network Supporting Multiple Learning Rules.

Artificial intelligence (AI) is changing the way computing is performed to cope with real-world, ill-defined tasks for which traditional algorithms fail. AI requires significant memory access, thus running into the von Neumann bottleneck when implemented in standard computing platforms. In this respect, low-latency energy-efficient in-memory computing can be achieved by exploiting emerging memristive devices, given their ability to emulate synaptic plasticity, which provides a path to design large-scale brain-inspired spiking neural networks (SNNs).

Standards for the Characterization of Endurance in Resistive Switching Devices.

Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices in commercial circuits is related to variability and reliability issues, which are usually evaluated through switching endurance tests. However, we note that most studies that claimed high endurances >10 cycles were based on resistance cycle plots that contain very few data points (in many cases even <20), and which are collected in only one device.

Multi-Input Logic-in-Memory for Ultra-Low Power Non-Von Neumann Computing.

Logic-in-memory (LIM) circuits based on the material implication logic (IMPLY) and resistive random access memory (RRAM) technologies are a candidate solution for the development of ultra-low power non-von Neumann computing architectures. Such architectures could enable the energy-efficient implementation of hardware accelerators for novel edge computing paradigms such as binarized neural networks (BNNs) which rely on the execution of logic operations. In this work, we present the multi-input IMPLY operation implemented on a recently developed smart IMPLY architecture, SIMPLY, which improves the circuit reliability, reduces energy consumption, and breaks the strict design trade-offs of conventional architectures.

On the Modeling of the Donor/Acceptor Compensation Ratio in Carbon-Doped GaN to Univocally Reproduce Breakdown Voltage and Current Collapse in Lateral GaN Power HEMTs.

The intentional doping of lateral GaN power high electron mobility transistors (HEMTs) with carbon (C) impurities is a common technique to reduce buffer conductivity and increase breakdown voltage. Due to the introduction of trap levels in the GaN bandgap, it is well known that these impurities give rise to dispersion, leading to the so-called "current collapse" as a collateral effect. Moreover, first-principles calculations and experimental evidence point out that C introduces trap levels of both acceptor and donor types.

Advanced Data Encryption ​using 2D Materials.

Advanced data encryption requires the use of true random number generators (TRNGs) to produce unpredictable sequences of bits. TRNG circuits with high degree of randomness and low power consumption may be fabricated by using the random telegraph noise (RTN) current signals produced by polarized metal/insulator/metal (MIM) devices as entropy source. However, the RTN signals produced by MIM devices made of traditional insulators, i.

Electroforming in Metal-Oxide Memristive Synapses.

Memristors have shown an extraordinary potential to emulate the plastic and dynamic electrical behaviors of biological synapses and have been already used to construct neuromorphic systems with in-memory computing and unsupervised learning capabilities; moreover, the small size and simple fabrication process of memristors make them ideal candidates for ultradense configurations. So far, the properties of memristive electronic synapses (i.e.

Multiscale Modeling for Application-Oriented Optimization of Resistive Random-Access Memory.

Memristor-based neuromorphic systems have been proposed as a promising alternative to von Neumann computing architectures, which are currently challenged by the ever-increasing computational power required by modern artificial intelligence (AI) algorithms. The design and optimization of memristive devices for specific AI applications is thus of paramount importance, but still extremely complex, as many different physical mechanisms and their interactions have to be accounted for, which are, in many cases, not fully understood. The high complexity of the physical mechanisms involved and their partial comprehension are currently hampering the development of memristive devices and preventing their optimization.

Understanding Current Instabilities in Conductive Atomic Force Microscopy.

Conductive atomic force microscopy (CAFM) is one of the most powerful techniques in studying the electrical properties of various materials at the nanoscale. However, understanding current fluctuations within one study (due to degradation of the probe tips) and from one study to another (due to the use of probe tips with different characteristics), are still two major problems that may drive CAFM researchers to extract wrong conclusions. In this manuscript, these two issues are statistically analyzed by collecting experimental CAFM data and processing them using two different computational models.