Introduction

The explosion of information in the era of big data and the Internet of Things poses great challenges for von Neumann machines and sensors \cite{shavit2014,malliaras2018,lee2020,huang2020,huang2019}, requiring new computing paradigms to meet the requirements of energy efficiency and big data workloads. Hardware-based neuromorphic computing, which mimics the operating principles and architectures of the brain through physical devices, is considered as one of the most promising platforms for big data computing as it has the potential to provide lower neergy consumtpion and more efficient computing than the von Neumann machine in the future\cite{smith2010,lu2018,service2014,huang2019a}. So far, plenty of devices have been reported with synapse and neural-like functions\cite{yang2016,markram2006,machens2012}, e.g., two-terminal memristors and three/multi-terminal transistors. Artificial neural networks based on an array of them have also been demonstrated for both low-level and high-level information processing\cite{han2019,park2018,wu2019,yang2018,huang2018,huang2021}.  Among these neuromorphic devices, three/multi-terminal neuromorphic transistors have attracted much attention due to their high degree of control freedom, enabling many complex neural and synaptic functions, such as dendritic integration\cite{zhao2021,hwang2017,datta2020,salleo2017}. Nonetheless, the development of three -terminal neuromorphic transistors is still in its infancy, and the capabilities of the devices need to be further expanded to achieve more interesting and useful applications.
On the other hand, in bio-synapse, the receptors on the surface of the post-membrane can be activated by transmitters released from the pre-membrane\cite{regehr2002,muller2000,2001,wang2004}. The responsivity of the synapse to the stimulus depends on the receptor activity and the receptor number on the post-synaptic membrane. The illness of human emotions (mood disorders, depression, and stress)\cite{duman2002,krystal2016,wang2008} usually influences the synaptic plasticity in the human brain, which would further influence the memory and learning behaviors of the human\cite{shekhar2004,jongens2005,kato2007}. The emulation of the receptor-tuning synaptic behavior is essential for a detailed understanding of the mechanism of synaptic-behavior modulation. The multi-terminal regulation ability of neuromorphic transistors gives us the ability to simulate the human brain to learn emotional regulation, but there are few related research reports.
Two-dimensional metal-organic frameworks (2D-MOFs) with a periodic network structure are composed of metal clusters or metal ions and organic ligands\cite{han2019a,foster2020,wang2020}. The large specific surface area, stable crystal structure, and highly accessible active sites of 2D MOFs enable them with enormous application potential in a variety of fields, including catalysis, energy storage, gas separation, etc\cite{zhou2020,liu2021,huang2018a,yan2021,li2021,lu2022,pang2021}. Recently, there are some reports about using MOFs as semiconducting materials and active layers for transistor and memory device fabrication, opening the door for using MOFs in electronic devices\cite{li2015,chen2016,grzybowski2014,xu2017}. However, few studies have explored the application potential of 2D-MOFs in optoelectronic neuromorphic computing devices.
Herein, we designed a 2D-MOFs/poly(methyl methacrylate) (PMMA) based optical-tunning dielectric layer for the fabrication of drain-tunable neuromorphic transistors. In addition to typical light-stimulated behaviors (paired-pulse facilitation (PPF) and excitatory postsynaptic current (EPSC)), the level of source-drain voltage can be utilized to model the number of receptors on the post-membrane and control the behavior of the device in response to prestimulation\cite{bolshakov2010}. The emotion-dependent learning efficiency is also successfully demonstrated by our synaptic device via tuning the energy band alignment by changing the source-drain voltage (from -3 to -25 V). When the source-drain voltage (VDS) is decreased to -1 V, the normal light-perception behavior of the device is completely depressed, which can be regarded as a human emotional illness. We also built a single-layer perceptron neural network based on the extracted parameters from our device, and demonstrate the emotion-tunable learning capability of the neural network. This work can not only broaden the application scenarios of 2D-MOFs but also further advance the development of neuromorphic electronics.

Experimental Methods

Materials Preparation

The 2D Zn2(ZnTCPP) MOFs were synthesized according to the previous report. Tetrakis(4-carboxyphenyl)porphyrin (TCPP) was purchased from TCI Inc. and used without any further purification. TCPP and zinc nitrate (Zn(NO3)2) was dissolved in a mixed solvent (N, N-Dimethylformamide: ethanol = 3:1) and heated at 80℃ for 24 hours. Purple crystals can be observed after the sample was centrifugated at 4500 rpm for 10 min and washed with ethanol in 3 times. In order to obtain 2D Zn2(ZnTCPP) MOFs, 20 mg of the as-prepared MOF (Zn2(ZnTCPP)) was added to 4 mL chlorobenzene (CB) and was then sonicated using an ultrasonic bath machine filled with water.  The temperature was maintained at 15-20℃. After the sonication, the resulted samples were centrifuged at 1500 rpm for 10 minutes to remove the large particles.

Device Fabrication

OFETs were fabricated using a silicon wafer with 300 nm silicon dioxide as substrate. 20 mg PMMA was added to the 1 mL 2D Zn2(ZnTCPP) MOFs solution then was stirred for 6 hours to obtain a uniform solution. The solution was spin-coated on the washed substrate at 2000 rpm for 60 s. The pentacene was then thermally evaporated onto the 2D Zn2(ZnTCPP) MOF-PMMA film at a rate of 0.1~0.3 Å/s. After that, 50 nm Au was thermally evaporated onto the pentacene film through a shadow mask as source-drain electrodes. The channel length and width were 30 μm and 1 mm, respectively.

Device Characterization

The surface morphology of pentacene and MOF-PMMA films were investigated by atomic force microscopy (Dimension Icon, Bruker). The thickness of 2D Zn2(ZnTCPP) MOF-PMMA film was obtained from AFM. The device characteristics and synaptic behaviors measurement were carried out using a Keithley 4200-SCS instrument at room temperature. For the characterization of the synaptic phototransistors, a light source (white light, Thorlabs MCWHL5-C4) was used. The optical intensities were calibrated with an optical power meter (Thorlabs PM100D).