The massive parallel approach of neuromorphic circuits leads to effective methods for solving complex problems. It has turned out that resistive switching devices with a continuous resistance range are potential candidates for such applications. These devices are memristive systems -nonlinear resistors with memory. They are fabricated in nanotechnology and hence parameter spread during fabrication may aggravate reproducible analyses. This issue makes simulation models of memristive devices worthwhile.Kinetic Monte-Carlo simulations based on a distributed model of the device can be used to understand the underlying physical and chemical phenomena. However, such simulations are very time-consuming and neither convenient for investigations of whole circuits nor for real-time applications, e.g. emulation purposes. Instead, a concentrated model of the device can be used for both fast simulations and real-time applications, respectively. We introduce an enhanced electrical model of a valence change mechanism (VCM) based double barrier memristive device (DBMD) with a continuous resistance range. This device consists of an ultra-thin memristive layer sandwiched between a tunnel barrier and a Schottky-contact. The introduced model leads to very fast simulations by using usual circuit simulation tools while maintaining physically meaningful parameters.Kinetic Monte-Carlo simulations based on a distributed model and experimental data have been utilized as references to verify the concentrated model.
SummaryMemristive devices are nonlinear resistors with memory. Due to the memory effect, those devices are potential candidates for self‐organizing circuits capable of learning from environmental influences in the past. The complexity of single devices with memory in combination with the required huge number of these devices in circuits including them make preinvestigations based on simulations very inefficient and time‐consuming. Flexible and real‐time capable memristive emulators, which can directly be incorporated into real circuits, can overcome this problem.In our approach, we introduce a general memristor emulator based on wave digital principles. The proposed emulator is flexible, robust, efficient, and it preserves the passivity of the real device in a digital signal‐processing sense. All these properties result in a reusable emulator, independent of a particular application. This work lists the wave digital emulations of different models from ideal to extended memristors. As an example for an extended memristor, the wave digital emulation of a double barrier memristive device is demonstrated.
Memristors-nonlinear resistors with memory-are potential candidates for the utilization in self-organizing circuits. These novel elements need innovative technological developments in order to incorporate them into integrated electrical circuits. Alternatively, HfO 2 -based resistive random access memories (RRAMs) are complementary metal-oxide-semiconductor (CMOS) compatible and can also be interpreted as memristive devices. A fingerprint of such devices is their rapid change between the high-and low-resistance state. It is intended to exploit this feature in self-organizing circuits to achieve a desired overall functionality. But the huge device variabilities exacerbate preinvestigations of real circuits including such devices. To this end, a wave digital model based on a mathematical and a physical model of a hafnium oxide (HfO 2 ) memristor is introduced. Utilizing the wave digital approach yields a flexible, real-time capable algorithmic model, which is suitable for live parameter fitting in real circuits. Comparisons of the emulated hysteresis with measured data of RRAM cells verify the functionality of the presented emulator. The proposed method for emulating physical systems is not restricted to single devices. Indeed, whole subcircuits can be emulated before fabrication in order to save development costs. KEYWORDSmemristor emulator, memristive devices, resistive switching, wave digital filter Int J Numer Model. 2019;32:e2588.wileyonlinelibrary.com/journal/jnm
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