Abstract Spiking neural network (SNN) in future neuromorphic architectures requires hardware devices to be not only capable of emulating fundamental functionalities of biological synapse such as spike-timing dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP), but also biodegradable to address current ecological challenges of electronic waste. Among different device technologies and materials, memristive synaptic devices based on natural organic materials have emerged as the favourable candidate to meet these demands. The metal–insulator-metal structure is analogous to biological synapse with low power consumption, fast switching speed and simulation of synaptic plasticity, while natural organic materials are water soluble, renewable and environmental friendly. In this study, the potential of a natural organic material—honey-based memristor for SNNs was demonstrated. The device exhibited forming-free bipolar resistive switching, a high switching speed of 100 ns set time and 500 ns reset time, STDP and SRDP learning behaviours, and dissolving in water. The intuitive conduction models for STDP and SRDP were proposed. These results testified that honey-based memristive synaptic devices are promising for SNN implementation in green electronics and biodegradable neuromorphic systems.
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Tuning Resistive Switching Behavior by Controlling Internal Ionic Dynamics for Biorealistic Implementation of Synaptic Plasticity
Memristive devices have demonstrated rich switching behaviors that closely resemble synaptic functions and provide a building block to construct efficient neuromorphic systems. It is demonstrated that resistive switching effects are controlled not only by the external field, but also by the dynamics of various internal state variables that facilitate the ionic processes. The internal temperature, for example, works as a second‐state variable to regulate the ion motion and provides the internal timing mechanism for the native implementation of timing‐ and rate‐based learning rules such as spike timing dependent plasticity (STDP). In this work, it is shown that the 2nd state‐variable in a Ta2O5‐based memristor, its internal temperature, can be systematically engineered by adjusting the material properties and device structure, leading to tunable STDP characteristics with different time constants. When combined with an artificial post‐synaptic neuron, the 2nd‐order memristor synapses can spontaneously capture the temporal correlation in the input streaming events.
more » « less- NSF-PAR ID:
- 10370016
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Electronic Materials
- Volume:
- 8
- Issue:
- 8
- ISSN:
- 2199-160X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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By mimicking biomimetic synaptic processes, the success of artificial intelligence (AI) has been astounding with various applications such as driving automation, big data analysis, and natural-language processing.[1-4] Due to a large quantity of data transmission between the separated memory unit and the logic unit, the classical computing system with von Neumann architecture consumes excessive energy and has a significant processing delay.[5] Furthermore, the speed difference between the two units also causes extra delay, which is referred to as the memory wall.[6, 7] To keep pace with the rapid growth of AI applications, enhanced hardware systems that particularly feature an energy-efficient and high-speed hardware system need to be secured. The novel neuromorphic computing system, an in-memory architecture with low power consumption, has been suggested as an alternative to the conventional system. Memristors with analog-type resistive switching behavior are a promising candidate for implementing the neuromorphic computing system since the devices can modulate the conductance with cycles that act as synaptic weights to process input signals and store information.[8, 9]
The memristor has sparked tremendous interest due to its simple two-terminal structure, including top electrode (TE), bottom electrode (BE), and an intermediate resistive switching (RS) layer. Many oxide materials, including HfO2, Ta2O5, and IGZO, have extensively been studied as an RS layer of memristors. Silicon dioxide (SiO2) features 3D structural conformity with the conventional CMOS technology and high wafer-scale homogeneity, which has benefited modern microelectronic devices as dielectric and/or passivation layers. Therefore, the use of SiO2as a memristor RS layer for neuromorphic computing is expected to be compatible with current Si technology with minimal processing and material-related complexities.
In this work, we proposed SiO2-based memristor and investigated switching behaviors metallized with different reduction potentials by applying pure Cu and Ag, and their alloys with varied ratios. Heavily doped p-type silicon was chosen as BE in order to exclude any effects of the BE ions on the memristor performance. We previously reported that the selection of TE is crucial for achieving a high memory window and stable switching performance. According to the study which compares the roles of Cu (switching stabilizer) and Ag (large switching window performer) TEs for oxide memristors, we have selected the TE materials and their alloys to engineer the SiO2-based memristor characteristics. The Ag TE leads to a larger memory window of the SiO2memristor, but the device shows relatively large variation and less reliability. On the other hand, the Cu TE device presents uniform gradual switching behavior which is in line with our previous report that Cu can be served as a stabilizer, but with small on/off ratio.[9] These distinct performances with Cu and Ag metallization leads us to utilize a Cu/Ag alloy as the TE. Various compositions of Cu/Ag were examined for the optimization of the memristor TEs. With a Cu/Ag alloying TE with optimized ratio, our SiO2based memristor demonstrates uniform switching behavior and memory window for analog switching applications. Also, it shows ideal potentiation and depression synaptic behavior under the positive/negative spikes (pulse train).
In conclusion, the SiO2memristors with different metallization were established. To tune the property of RS layer, the sputtering conditions of RS were varied. To investigate the influence of TE selections on switching performance of memristor, we integrated Cu, Ag and Cu/Ag alloy as TEs and compared the switch characteristics. Our encouraging results clearly demonstrate that SiO2with Cu/Ag is a promising memristor device with synaptic switching behavior in neuromorphic computing applications.
Acknowledgement This work was supported by the U.S. National Science Foundation (NSF) Award No. ECCS-1931088. S.L. and H.W.S. acknowledge the support from the Improvement of Measurement Standards and Technology for Mechanical Metrology (Grant No. 22011044) by KRISS.
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et al. , Physica Status Solidi (RRL) - Rapid Research Letters, pssr.202200075R1, In press, 2022. -
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Electrochemical‐based memristors are highly attractive that are capable of nonvolatile analog tuning, long‐term state stability, low power consumption, device scalability, and fast switching speeds. Through the combination of film deposition techniques, i.e., vapor phase polymerization and screen printing, fabrication of a poly(4‐(6‐hexyl)‐4
H ‐dithieno[3,2‐b :2′,3′‐d ]pyrrole) (p6DTP)‐based synaptic‐emulating three‐terminal memristor is designed. Through voltage‐driven pulse programming, and square waves with an amplitude of 100 mV and duration of 100 msec, the device exhibits a power consumption of 1 pJmm−2per synaptic event. By analyzing the fundamental operational trends of the p6DTP‐based device, simple and advanced integrated applications can be demonstrated along with synaptic‐like responses. This effort is the first presentation of the vapor phase polymerization technique for any dithienopyrrole‐based monomers, along with the physical implementation of any memristive system as an advanced logical circuit, demonstrated here as a cascaded combinational logic gate. -
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Abstract Memristive systems offer biomimetic functions that are being actively explored for energy‐efficient neuromorphic circuits. In addition to providing ultimate geometric scaling limits, 2D semiconductors enable unique gate‐tunable responses including the recent realization of hybrid memristor and transistor devices known as memtransistors. In particular, monolayer MoS2memtransistors exhibit nonvolatile memristive switching where the resistance of each state is modulated by a gate terminal. Here, further control over the memtransistor neuromorphic response through the introduction of a second gate terminal is gained. The resulting dual‐gated memtransistors allow tunability over the learning rate for non‐Hebbian training where the long‐term potentiation and depression synaptic behavior is dictated by gate biases during the reading and writing processes. Furthermore, the electrostatic control provided by dual gates provides a compact solution to the sneak current problem in traditional memristor crossbar arrays. In this manner, dual gating facilitates the full utilization and integration of memtransistor functionality in highly scaled crossbar circuits. Furthermore, the tunability of long‐term potentiation yields improved linearity and symmetry of weight update rules that are utilized in simulated artificial neural networks to achieve a 94% recognition rate of hand‐written digits.
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New parallel computing architectures based on neuromorphic computing are needed due to their advantages over conventional computation with regards to real‐time processing of unstructured sensory data such as image, video, or voice. However, developing artificial neuromorphic system remains a challenge due to the lack of electronic synaptic devices, which can mimic all the functions of biological synapses with low energy consumption. Here it is reported that two‐terminal organometal trihalide perovskite (OTP) synaptic devices can mimic the neuromorphic learning and remembering process. Various functions known in biological synapses are demonstrated in OTP synaptic devices including four forms of spike‐timing‐dependent plasticity (STDP), spike‐rate‐dependent plasticity (SRDP), short‐term plasticity (STP) and long‐term potentiation (LTP)), and learning‐experience behavior. The excellent photovoltaic property of the OTP devices also enables photo‐read synaptic functions. The perovskite synapse has the potential of low energy consumption of femto‐Joule/(100 nm)2per event, which is close to the energy consumption of biological synapses. The demonstration of energy‐efficient OTP synaptic devices opens a new plausible application of OTP materials into neuromorphic devices, which offer the high connectivity and high density required for biomimic computing.
