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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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/SiOx/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/SiOx/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.
more » « less- Award ID(s):
- 1845331
- NSF-PAR ID:
- 10488590
- Publisher / Repository:
- NPJ 2D materials and applications
- Date Published:
- Journal Name:
- npj 2D Materials and Applications
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2397-7132
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
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Neurotransmitters are small molecules involved in neuronal signaling and can also serve as stress biomarkers.1Their abnormal levels have been also proposed to be indicative of several neurological diseases such as Alzheimer’s disease, Parkinson’s disease, Huntington disease, among others. Hence, measuring their levels is highly important for early diagnosis, therapy, and disease prognosis. In this work, we investigate facile functionalization methods to tune and enhance sensitivity of printed graphene sensors to neurotransmitters. Sensors based on direct laser scribing and screen-printed graphene ink are studied. These printing methods offer ease of prototyping and scalable fabrication at low cost.
The effect of functionalization of laser induced graphene (LIG) by electrodeposition and solution-based deposition of TMDs (molybdenum disulfide2and tungsten disulfide) and metal nanoparticles is studied. For different processing methods, electrochemical characteristics (such as electrochemically active surface area: ECSA and heterogenous electron transfer rate: k0) are extracted and correlated to surface chemistry and defect density obtained respectively using X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy. These functionalization methods are observed to directly impact the sensitivity and limit of detection (LOD) of the graphene sensors for the studied neurotransmitters. For example, as compared to bare LIG, it is observed that electrodeposition of MoS2on LIG improves ECSA by 3 times and k0by 1.5 times.3Electrodeposition of MoS2also significantly reduces LOD of serotonin and dopamine in saliva, enabling detection of their physiologically relevant concentrations (in pM-nM range). In addition, chemical treatment of LIG sensors is carried out in the form of acetic acid treatment. Acetic acid treatment has been shown previously to improve C-C bonds improving the conductivity of LIG sensors.4In our work, in particular, acetic acid treatment leads to larger improvement of LOD of norepinephrine compared to MoS2electrodeposition.
In addition, we investigate the effect of plasma treatment to tune the sensor response by modifying the defect density and chemistry. For example, we find that oxygen plasma treatment of screen-printed graphene ink greatly improves LOD of norepinephrine up to three orders of magnitude, which may be attributed to the increased defects and oxygen functional groups on the surface as evident by XPS measurements. Defects are known to play a key role in enhancing the sensitivity of 2D materials to surface interactions, and have been explored in tuning/enhancing the sensor sensitivity.5Building on our previous work,3we apply a custom machine learning-based data processing method to further improve that sensitivity and LOD, and also to automatically benchmark different molecule-material pairs.
Future work includes expanding the plasma chemistry and conditions, studying the effect of precursor mixture in laser-induced solution-based functionalization, and understanding the interplay between molecule-material system. Work is also underway to improve the machine learning model by using nonlinear learning models such as neural networks to improve the sensor sensitivity, selectivity, and robustness.
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Sci. Adv. 6 , 4250–4257 (2020).V. Kammarchedu, D. Butler, A. Ebrahimi,
Anal. Chim. Acta .1232 , 340447 (2022).H. Yoon, J. Nah, H. Kim, S. Ko, M. Sharifuzzaman, S. C. Barman, X. Xuan, J. Kim, J. Y. Park,
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Neuromorphic computing has the great potential to enable faster and more energy‐efficient computing by overcoming the von Neumann bottleneck. However, most emerging nonvolatile memory (NVM)‐based artificial synapses suffer from insufficient precision, nonlinear synaptic weight update, high write voltage, and high switching latency. Moreover, the spatiotemporal dynamics, an important temporal component for cognitive computing in spiking neural networks (SNNs), are hard to generate with existing complementary metal–oxide–semiconductor (CMOS) devices or emerging NVM. Herein, a three‐terminal, Li
x WO3‐based electrochemical synapse (LiWES) is developed with low programming voltage (0.2 V), fast programming speed (500 ns), and high precision (1024 states) that is ideal for artificial neural networks applications. Time‐dependent synaptic functions such as paired‐pulse facilitation (PPF) and temporal filtering that are critical for SNNs are also demonstrated. In addition, by leveraging the spike‐encoded timing information extracted from the short‐term plasticity (STP) behavior in the LiWES, an SNNs model is built to benchmark the pattern classification performance of the LiWES, and the result indicates a large boost in classification performance (up to 128×), compared with those NO‐STP synapses. -
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Abstract Optoelectronic synapses combine the functionalities of a non-volatile memory and photodetection in the same device, paving the path for the realization of artificial retina systems which can capture, pre-process, and identify images on the same platform. Graphene/Ta2O5/graphene phototransistor exhibits synapse characteristics when visible electromagnetic radiation of wavelength 405 nm illuminates the device. The photocurrent is retained after light withdrawal when positive gate voltage is applied to the device. The device exhibits distinct conductance states, modulated by different parameters of incident light, such as pulse width and number of pulses. The conductance state can be retained for 104 s, indicating long term potentiation (LTP), similar to biological synapses. By using optical and electrical pulses, the device shows optical potentiation and electrical LTD repeatably, implying their applicability in neural networks for pattern recognition.
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Abstract Specialized hardware for neural networks requires materials with tunable symmetry, retention, and speed at low power consumption. The study proposes lithium titanates, originally developed as Li‐ion battery anode materials, as promising candidates for memristive‐based neuromorphic computing hardware. By using ex‐ and in operando spectroscopy to monitor the lithium filling and emptying of structural positions during electrochemical measurements, the study also investigates the controlled formation of a metallic phase (Li7Ti5O12) percolating through an insulating medium (Li4Ti5O12) with no volume changes under voltage bias, thereby controlling the spatially averaged conductivity of the film device. A theoretical model to explain the observed hysteretic switching behavior based on electrochemical nonequilibrium thermodynamics is presented, in which the metal‐insulator transition results from electrically driven phase separation of Li4Ti5O12and Li7Ti5O12. Ability of highly lithiated phase of Li7Ti5O12for Deep Neural Network applications is reported, given the large retentions and symmetry, and opportunity for the low lithiated phase of Li4Ti5O12toward Spiking Neural Network applications, due to the shorter retention and large resistance changes. The findings pave the way for lithium oxides to enable thin‐film memristive devices with adjustable symmetry and retention.
