Conductive bridging random access memory (CBRAM) has been considered to lớn be a promising emerging device for artificial synapses in neuromorphic computing systems. Good analog synaptic behaviors, such as linear & symmetric synapse updates, are desirable khổng lồ provide high learning accuracy. Although numerous efforts have been made to develop analog CBRAM for years, the stochastic & 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 & 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 và nanocluster formation in the upper SiNx layer và 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, và 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.
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Introduction
As computing paradigms are shifting from a central processing unit-centric system to a data-centric system, a new computing architecture is demanded khổng lồ overcome the performance limitations in the present von Neumann architecture1,2,3,4,5. Bioinspired neuromorphic computing is one of the alternatives to von Neumann computing. Emerging devices for artificial neurons và synapses as basic building blocks in neuromorphic systems have been extensively studied because many of them have high potential in terms of nguồn consumption, scalability, and computation speed in comparison with complementary metal–oxide–semiconductor (CMOS)-based neurons and synapses4,6,7,8,9. The ideal analog behavior of artificial synapses is one of the key factors for high learning accuracy of neuromorphic systems based on artificial neural network algorithms. Linear và symmetric synapse conductance updates under identical spikes as well as a large on/off ratio are required to lớn implement ideal analog synaptic devices10.
Recently, several different approaches, such as flash memory, phase change memory (PCM), ferroelectric field effect transistors (FeFETs), và resistive random access memory (ReRAM), have been investigated lớn realize ideal analog synaptic devices. Multibit flash memory is one of the promising candidates, but the scaling issue due to its large footprint is a concern. Additionally, the programming speed and endurance of NAND flash memory cannot yet meet the requirements of neuromorphic applications10. In many studies, PCM has been demonstrated lớn act as analog synapses due lớn its high speed and good scalability, but the inherently high nonlinearity of the synapse weight update, especially in depression, poses a challenge khổng lồ the implementation of ideal analog behavior11. An FeFET is another promising solution lớn achieve high linearity of the synapse weight update because the device conductance is modulated through a gate electrode that does not interfere with a current-conducting channel12,13. However, the implementation of FeFETs using conventional perovskite-type oxide materials finds difficulty in securing CMOS process compatibility14,15.
ReRAM, where conductive filaments constitute conductive metal ions or oxygen vacancies in the resistive switching layer, provides excellent performance in terms of scalability, low switching current, endurance, & retention. Furthermore, the simple fabrication process và CMOS back-end of line compatibility make it a promising candidate for artificial synapses in neuromorphic systems, as reported in the literature16. Among various types of ReRAM devices, conductive bridging random access memory (CBRAM) has been suggested for artificial synapses because it has high potential, such as a large on/off ratio, a long retention time, & high speed16. However, the abrupt conductance changes due to lớn the bridging & rupture of a nanoscale conductive filament cause highly nonlinear & asymmetrical conductance responses. This makes it difficult for CBRAM lớn be applied as ideal analog synapses in a neuromorphic system.
Various approaches have been proposed to lớn suppress the abrupt trabzondanbak.com of the filamentary switching of CBRAM devices: (i) multiple weak filament implementation, (ii) internal current limiting, và (iii) filament modulation. For implementation of multiple weak filaments, nanoscale metal particles are incorporated into a switching layer by cosputtering of metal & dielectric materials so that spatially uniform conduction can be formed between active metal-rich và active metal-poor regions through conductive multifilaments5. Annealing an active metal on a switching layer can be another approach khổng lồ produce multiple weak filaments. The metal ions diffuse into the switching layer by annealing, achieving stochastic multiple conduction channels17. As multiple conductive ion transport channels are preformed in the switching layer in the case of multiple weak filaments, this approach suppresses abrupt strong filament formation và provides more reliable & gradual switching4. In the internal current limiting case, ReRAM includes a resistive layer as an internal current limiter in series with a switching layer18. The internal current limiter helps suppress the abrupt increase in the device current through the switching layer during the phối transition via a so-called voltage divider effect4,19. In the filament modulation method, the diameter of the conductive filament is gradually adjusted in the mix mode by additional voltage pulses. By controlling the conductive filament kích thước successively, the device shows a gradual conductance change20.
In this study, we demonstrate a novel approach khổng lồ enhance the analog behavior of a (Ag đứng top electrode (TE))/SiNx/a-Si/(p++ say đắm bottom electrode)-based CBRAM device through Ge implantation. Depending on the voltage regime, the CBRAM device exhibits binary or analog switching behavior. Ge forms an ideal solid solution blended with Si. Therefore, the Ge implanted into the a-Si layer does not form any second phase materials. Furthermore, Ge has a higher atomic weight than Si, so it generates more defects by implantation. These are the main reasons why we chose Ge as the implantation ion.
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The process of implantation in resistive switching devices has been introduced for various purposes. Some metal oxides, such as CuOx & HfOx, were synthesized at low temperature by implanting oxygen21,22. Metal or gas ions, such as oxygen, nitrogen, Au, Zr, & Ti, were also implanted in an effort to improve the resistive memory parameters, including the retention time, device yield, variations and forming voltage23,24,25. Unlike previous research, in this study, we introduced the implantation process to adjust the conductance of a current limiting layer & metal cluster morphology. If a sufficiently high voltage greater than the forming voltage is applied, the device becomes electrically formed & exhibits abrupt binary switching. By contrast, when a low voltage below the forming voltage is applied, the device shows a typical analog memristor behavior. In this case, the a-Si layer serves as an internal current limiter khổng lồ suppress the abrupt strong filament formation, và the SiNx đứng top layer serves as a switching layer. The SiNx/a-Si bilayer device shows analog behavior, but the nonlinearity of the conductance update, on/off ratio, và retention time are not desirable. In an effort to lớn enhance the analog behavior of the CBRAM device, we introduced Ge implantation into the a-Si underlayer to modify the conductance và surface structure of the a-Si layer. Because of Ge implantation, the analog behavior in terms of the linearity, on-off ratio, & retention time was remarkably enhanced. We discuss the origin of the enhanced analog behavior through implantation. Last, we also perform an MNIST recognition simulation in memristor-based neural networks with consideration of the synapse nonlinearity. The implanted CBRAM device provided a learning accuracy of 91.3% due lớn its enhanced linearity of the synapse weight update, whereas the unimplanted device exhibited only a 62.8% accuracy.