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1. Optoelectronic synapse using monolayer MoS2 field effect transistors

   https://doi.org/10.1038/s41598-020-78767-4  

   Islam, Molla Manjurul ; Dev, Durjoy ; Krishnaprasad, Adithi ; Tetard, Laurene ; Roy, Tania ( December 2020 , Scientific Reports)

   null (Ed.)

   Abstract Optical data sensing, processing and visual memory are fundamental requirements for artificial intelligence and robotics with autonomous navigation. Traditionally, imaging has been kept separate from the pattern recognition circuitry. Optoelectronic synapses hold the special potential of integrating these two fields into a single layer, where a single device can record optical data, convert it into a conductance state and store it for learning and pattern recognition, similar to the optic nerve in human eye. In this work, the trapping and de-trapping of photogenerated carriers in the MoS 2 /SiO 2 interface of a n-channel MoS 2 transistor was employed to emulate the optoelectronic synapse characteristics. The monolayer MoS 2 field effect transistor (FET) exhibits photo-induced short-term and long-term potentiation, electrically driven long-term depression, paired pulse facilitation (PPF), spike time dependent plasticity, which are necessary synaptic characteristics. Moreover, the device’s ability to retain its conductance state can be modulated by the gate voltage, making the device behave as a photodetector for positive gate voltages and an optoelectronic synapse at negative gate voltages. 

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2. Low‐Power, Electrochemically Tunable Graphene Synapses for Neuromorphic Computing

   https://doi.org/10.1002/adma.201802353  

   Sharbati, Mohammad Taghi ; Du, Yanhao ; Torres, Jorge ; Ardolino, Nolan D. ; Yun, Minhee ; Xiong, Feng ( July 2018 , Advanced Materials)

   Brain‐inspired neuromorphic computing has the potential to revolutionize the current computing paradigm with its massive parallelism and potentially low power consumption. However, the existing approaches of using digital complementary metal–oxide–semiconductor devices (with “0” and “1” states) to emulate gradual/analog behaviors in the neural network are energy intensive and unsustainable; furthermore, emerging memristor devices still face challenges such as nonlinearities and large write noise. Here, an electrochemical graphene synapse, where the electrical conductance of graphene is reversibly modulated by the concentration of Li ions between the layers of graphene is presented. This fundamentally different mechanism allows to achieve a good energy efficiency (<500 fJ per switching event), analog tunability (>250 nonvolatile states), good endurance, and retention performances, and a linear and symmetric resistance response. Essential neuronal functions such as excitatory and inhibitory synapses, long‐term potentiation and depression, and spike timing dependent plasticity with good repeatability are demonstrated. The scaling study suggests that this simple, two‐dimensional synapse is scalable in terms of switching energy and speed.

    

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3. Using Light for Better Programming of Ferroelectric Devices: Optoelectronic MoS 2 ‐Pb(Zr,Ti)O 3 Memories with Improved On–Off Ratios

   https://doi.org/10.1002/aelm.202001223  

   Lipatov, Alexey ; Vorobeva, Nataliia S. ; Li, Tao ; Gruverman, Alexei ; Sinitskii, Alexander ( April 2021 , Advanced Electronic Materials)

   Ferroelectric (FE) devices are conventionally switched by an application of an electric field. However, the recent discoveries of light–matter interactions in heterostructures based on 2D semiconductors and FE materials open new opportunities for using light as an additional tool for device programming. Recently, a purely optical switching of FE polarization in heterostructures comprising 2D MoS₂and FE oxide perovskites, such as BaTiO₃and Pb(Zr,Ti)O₃(PZT), was demonstrated. In this work, it is investigated whether this optical switching has a practical value and can be used to improve functional characteristics of MoS₂‐PZT FE field‐effect transistors for nonvolatile memory applications. It is demonstrated that the combined use of an electrical field and visible light improves the nonvolatile ON/OFF ratios in MoS₂‐PZT memories by several orders of magnitude compared to their purely electrical operation. The memories are read at zero gate voltage (V_G) in darkness, but their ON and OFF currents, which routinely varied for different devices by over 10⁵, are achieved by programming at the sameV_G = −6 V with (ON state) and without (OFF state) light illumination, demonstrating its crucial importance. The light can likely serve as an important tool for better programming of a large variety of other semiconductor‐FE devices.

    

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4. Graphene/MoS2/SiOx memristive synapses for linear weight update

   https://doi.org/10.1038/s41699-023-00388-y  

   Krishnaprasad, Adithi ; Dev, Durjoy ; Shawkat, Mashiyat Sumaiya ; Martinez-Martinez, Ricardo ; Islam, Molla Manjurul ; Chung, Hee-Suk ; Bae, Tae-Sung ; Jung, Yeonwoong ; Roy, Tania ( December 2023 , npj 2D Materials and Applications)

   Memristors for neuromorphic computing have gained prominence over the years for implementing synapses and neurons due to their nano-scale footprint and reduced complexity. Several demonstrations show two-dimensional (2D) materials as a promising platform for the realization of transparent, flexible, ultra-thin memristive synapses. However, unsupervised learning in a spiking neural network (SNN) facilitated by linearity and symmetry in synaptic weight update has not been explored thoroughly using the 2D materials platform. Here, we demonstrate that graphene/MoS₂/SiO_x/Ni synapses exhibit ideal linearity and symmetry when subjected to identical input pulses, which is essential for their role in online training of neural networks. The linearity in weight update holds for a range of pulse width, amplitude and number of applied pulses. Our work illustrates that the mechanism of switching in MoS₂-based synapses is through conductive filaments governed by Poole-Frenkel emission. We demonstrate that the graphene/MoS₂/SiO_x/Ni synapses, when integrated with a MoS₂-based leaky integrate-and-fire neuron, can control the spiking of the neuron efficiently. This work establishes 2D MoS₂as a viable platform for all-memristive SNNs.

    

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5. Ferroelectric Tunneling Junctions Based on Aluminum Oxide/ Zirconium-Doped Hafnium Oxide for Neuromorphic Computing

   https://doi.org/10.1038/s41598-019-56816-x  

   Ryu, Hojoon ; Wu, Haonan ; Rao, Fubo ; Zhu, Wenjuan ( December 2019 , Scientific Reports)

   Ferroelectric tunneling junctions (FTJs) with tunable tunneling electroresistance (TER) are promising for many emerging applications, including non-volatile memories and neurosynaptic computing. One of the key challenges in FTJs is the balance between the polarization value and the tunneling current. In order to achieve a sizable on-current, the thickness of the ferroelectric layer needs to be scaled down below 5 nm. However, the polarization in these ultra-thin ferroelectric layers is very small, which leads to a low tunneling electroresistance (TER) ratio. In this paper, we propose and demonstrate a new type of FTJ based on metal/Al₂O₃/Zr-doped HfO₂/Si structure. The interfacial Al₂O₃layer and silicon substrate enable sizable TERs even when the thickness of Zr-doped HfO₂(HZO) is above 10 nm. We found that F-N tunneling dominates at read voltages and that the polarization switching in HZO can alter the effective tunneling barrier height and tune the tunneling resistance. The FTJ synapses based on Al₂O₃/HZO stacks show symmetric potentiation/depression characteristics and widely tunable conductance. We also show that spike-timing-dependent plasticity (STDP) can be harnessed from HZO based FTJs. These novel FTJs will have high potential in non-volatile memories and neural network applications.

    

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