Atomic Layer Deposited Hf0.5Zr0.5O2-based Flexible Memristor with Short/Long-Term Synaptic Plasticity
Artificial synapses are the fundamental of building a neuron network for neuromorphic computing to overcome the bottleneck of the von Neumann system. Based on a low-temperature atomic layer deposition process, a flexible electrical synapse was proposed and showed bipolar resistive switching characteristics. With the formation and rupture of ions conductive filaments path, the conductance was modulated gradually. Under a series of pre-synaptic spikes, the device successfully emulated remarkable short-term plasticity, long-term plasticity, and forgetting behaviors. Therefore, memory and learning ability were integrated to the single flexible memristor, which are promising for the next-generation of artificial neuromorphic computing systems.
KeywordsAtomic layer deposition Low-temperature process Flexible electronics Synaptic plasticity
Atomic layer deposition
The classical von Neumann computing scheme is suffering a bottleneck of information transfer between the processing center and storage units . Through emulating biological brains, neuromorphic computing has become an attractive candidate with the ability of learning and memory in one single system [2, 3]. Electronic synapses, with the ability of mimicking bio-synaptic behavior, are the foundation of neuromorphic systems. Recently, bio-synaptic behaviors have been emulated by various memristors, including two-terminal devices and novel three-terminal synaptic transistors based on ionic defects [4, 5]. With history-dependent conductance, memristors were reported to simulate the long-term depression (LTD) or potentiation (LTP), pair-pulse fluctuation (PPF), paired-pulse depression (PPD), and spike-timing-dependent plasticity (STDP) [6, 7, 8]. Especially, LTP/LTD is vital for face classification, digital recognition, and other artificial intelligence applications based on synaptic weight modification [9, 10, 11]. Originating from immediate post-synaptic current response, STP is widely used for information filtering and instantaneous signal transmission .
A variety of material systems were studied for artificial synapses with bio-synaptic plasticity, including HfO2, ZnO, WOx, TaOx, InGaZnO, organic polymers, and 2D transition-metal dichalcogenides (TMDCs) [13, 14, 15, 16, 17, 18, 19]. Among them, Hf0.5Zr0.5O2 (HZO) is one of the novel high-k materials and compatible with the process of complementary metal oxide semiconductor (CMOS) . Although HZO-based artifical synapstic devices have been reported, the high-temperature preparation process is hard to aviod [21, 22, 23].
On the other hand, flexible artificial synaptic devices were widely studied to satisfy the rising need for wearable artificial intelligence applications [24, 25]. However, the high-temperature preparation process is an impediment to the application of a flexible substrate. Although a transfer process was proposed to solve the problem, the high failure rate and wrinkle defects caused by transfer hinder the large-scale use of this method [26, 27]. It is worth noting that low-temperature processing has no damage to flexible substrates, which is an effective way of developing large-scale wearable synaptic arrays.
In this work, a low-temperature ALD technique for HZO-based memristor (PET/ITO/HZO/Ag) was developed. Gradual conductance switching process was demonstrated in this memristor. Based on gradual resistance switching characteristics, typical synaptic plasticity was emulated, including LTP/LTD, STP, PPF, and forgetting curves. With the function of biological synapses, the flexible HZO-based memristor is attractive for future applications in a neuromorphic computing system.
The electrical characteristics were performed using a semiconductor parameter analyzer (Agilent B1500A) in the atmospheric environment at room temperature. The bottom electrode was grounded while the programming bias was applied to the top electrode.
Results and Discussion
In summary, a flexible HZO-based artificial synaptic device was proposed based on low-temperature ALD. Typical bipolar resistive switching characteristics were demonstrated in this flexible memristor. By applying consecutive pulses in the top electrode, long-term plasticity and short-term plasticity were simulated by the electrical synapse, including LTP, LTD, PPF, PPD, and forgetting behaviors. Gradually modulated conductance could be attributed to controllable Ag ions conductive filament path. The flexible electrical synapse becomes one of the promising candidates for hardware implementation of neuromorphic circuits.
This work was supported by the NSFC (61704030 and 61522404), the 02 State Key Project (2017ZX02315005), the Program of Shanghai Subject Chief Scientist (18XD1402800), the Support Plans for the Youth Top-Notch Talents of China, and the “Chen Guang” project supported by the Shanghai Municipal Education Commission and Shanghai Education Development Foundation.
Availability of Data and Materials
All data are fully available without restriction.
T-YW prepared the HZO-based flexible artificial synaptic devices. T-YW and J-LM designed the electrical measurements method of synaptic plasticity. T-YW and Z-Y H carried out the bipolar resistive switching characteristics of the memristor. HZ and S-JD revised the manuscript. LC, Q-QS, and D-WZ supervised the whole work. All authors critically read and approved the final manuscript.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
- 20.Wang T, Yu L, Chen L, Liu H, Zhu H, Sun Q, Ding S, Zhou P, Zhang DW (2017) Atomic layer deposited Hf0.5Zr0.5O2-based flexible RRAM, 2017 IEEE 12th International Conference on ASIC (ASICON), pp 203–206Google Scholar
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.