Analog Synaptic Behavior of a Silicon Nitride Memristor

Abstract

In this paper, we present a synapse function using analog resistive-switching behaviors in a SiN-based memristor with a complementary metal-oxide-semiconductor compatibility and expandability to three-dimensional crossbar array architecture. A progressive conductance change is attainable as a result of the gradual growth and dissolution of the conducting path, and the series resistance of the AlO layer in the Ni/SiN/AlO/TiN memristor device enhances analog switching performance by reducing current overshoot. A continuous and smooth gradual reset switching transition can be observed with a compliance current limit (>100 μA), and is highly suitable for demonstrating synaptic characteristics. Long-term potentiation and long-term depression are obtained by means of identical pulse responses. Moreover, symmetric and linear synaptic behaviors are significantly improved by optimizing pulse response conditions, which is verified by a neural network simulation. Finally, we display the spike-timing-dependent plasticity with the multipulse scheme. This work provides a possible way to mimic biological synapse function for energy-efficient neuromorphic systems by using a conventional passive SiN layer as an active dielectric.

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

“…g) Variable channel conductance of an ionotronic memristor. (f) Reproduced with permission . Copyright 2017, American Chemical Society.…”

“…In the past few years, ionotronic transistors or memristors were proposed to mimic advanced neural functions. By connecting pressure sensors and oscilloscope with neuromorphic devices and adoping mathematical modeling and software simulation, advanced neural functions are simulated, including logic function, image memorization, pattern recognition, face recognition, classic conditioning, tactile‐perception system, etc. In this section, we shortly discuss these achievements.…”

“…Dispersed Au─Ag core-shell nanoparticles in the PVPy film lead to a more effective formation of conductive filament and demonstrate a good charge trapping and migration characteristics. The memristive device shows good controllable bistable RS states with an on/off ratio of [128] Copyright 2017, American Chemical Society. (g) Reproduced with permission.…”

Hierarchical Temporal Memory Using Memristor Networks: A Survey

“…The switching time of such memristors is 100ns − 200ns, which is relatively fast comparing to HP T iO 2 memristors [46]. However, the switching voltage is large (greater than 5V [45]). In addition, the memristor has not been tested for HTM applications.…”

Neuromemristive Circuits for Edge Computing: A Review

“…Several memristive devices are proven to be compatible with the CMOS fabrication process [137], [140]. While T iO 2−x memristors were quite popular, there are other growing list of memristors based on materials such as Hf O x , T aO x , M oO x , La 1x Sr x M nO 3 , InGaZnO [141], organic memristors with electrografted redox thin film [142], ferroelectric tunnel memristors (FTM), Ge 2 Sb 2 T e 5 (GST) memristors [132], SiO x [143], SiN x [141] and P r 0.7 Ca 0.3 M nO 3 (PCMO) [63]. As the memristor technology is only at early stages of development, the properties, stability issues, switching behavior and compatibility with CMOS devices of various memristive elements and selection of most stable material stack is an open problem.…”