Silicon-based inorganic-organic hybrid optoelectronic synaptic devices simulating cross-modal learning

Li, Yayao; Wang, Yue; Yin, Lei; Huang, Wen; Wei, Peng; Zhu, Yiyue; Wang, Kun; Yang, Deren; Pi, Xiaodong

doi:10.1007/s11432-020-3035-8

Cited by 23 publications

(23 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such a spatial separation of the photo-generated carriers gives rise to photogating for the synaptic transistor, in which holes are transported through P3HT. [38] As a result, the EPSC 1 continuously increases with the increase of the spike duration, similar to what happens to previous photogated synaptic transistors. [22,36,51] Figure 2a shows the EPSC 1 of the Si NCs/P3HT synaptic transistor stimulated by two identical successive optical spikes.…”

Section: Introductionsupporting

confidence: 73%

“…[19][20][21][22][23][24][25][26][27][28][29][30] State-of-the-art optoelectronic synaptic devices have either two-or three-terminal structures. [31][32][33][34][35][36][37][38] The two-terminal optoelectronic synaptic devices are simple, enabling high-density integration into a circuit, [39] while the three-terminal optoelectronic synaptic devices are capable of achieving complex functionalities by using multi-gate regulation. [11,40,41] It would be advantageous to have the synergy of the two-and three-terminal structures in one single optoelectronic synaptic device.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Dual‐Modal Optoelectronic Synaptic Devices with Versatile Synaptic Plasticity

Wang

Zhu

et al. 2021

Adv Funct Materials

Self Cite

View full text Add to dashboard Cite

Optoelectronic synaptic devices that mimic biological synapses are critical building blocks of artificial neural networks (ANN) based on optoelectronic integration. Here it is shown that an optoelectronic synaptic device based on the hybrid structure of silicon nanocrystals (Si NCs) and poly(3-hexylthiophene) (P3HT) can work with dual modes, exhibiting versatile synaptic plasticity. In the three-terminal mode, the device is a synaptic transistor, which has wavelength-selective synaptic plasticity due to potential wells enabled by the Si NCs/P3HT hybrid structure. In the two-terminal mode, it is a synaptic metal-oxide-semiconductor (MOS) device, which is capable of mimicking spike-rate-dependent plasticity (SRDP) and metaplasticity with optical stimulation. Based on the wavelength-selective synaptic plasticity a light-stimulated ANN is proposed to recognize handwritten digits with an accuracy of around 90.4%. In addition, the SRDP and metaplasticity may be well used for the simulation of edge detection of images, facilitating real-time image processing.

show abstract

Section: Introductionsupporting

confidence: 73%

mentioning

confidence: 99%

Dual‐Modal Optoelectronic Synaptic Devices with Versatile Synaptic Plasticity

Wang

Zhu

et al. 2021

Adv Funct Materials

Self Cite

View full text Add to dashboard Cite

show abstract

“…When a (3‐aminopropyl) trimethoxylsilane self‐assembled monolayer (SAM) was introduced between the channel and dielectric layers, the EPSC of the device enhanced and decayed faster than that without the hydrophilic SAM. [ 46 ] In this study, the contribution from interfacial traps is precluded by an octadecyltrimethoxysilane (ODTS) SAM between the channel and dielectric layers. Therefore, the P3HT‐based device presented a low photoresponse, and the synaptic performance of BCP‐based devices was presumably occurred in the channel.…”

Section: Resultsmentioning

confidence: 99%

Low‐Energy‐Consumption and Electret‐Free Photosynaptic Transistor Utilizing Poly(3‐hexylthiophene)‐Based Conjugated Block Copolymers

et al. 2022

View full text Add to dashboard Cite

Neuromorphic computation possesses the advantages of self-learning, highly parallel computation, and low energy consumption, and is of great promise to overcome the bottleneck of von Neumann computation. In this work, a series of poly(3-hexylthiophene) (P3HT)-based block copolymers (BCPs) with different coil segments, including polystyrene, poly(2-vinylpyridine) (P2VP), poly(2-vinylnaphthalene), and poly(butyl acrylate), are utilized in photosynaptic transistor to emulate paired-pulse facilitation, spike time/rate-dependent plasticity, short/long-term neuroplasticity, and learning−forgetting−relearning processes. P3HT serves as a carrier transport channel and a photogate, while the insulating coils with electrophilic groups are for charge trapping and preservation. Three main factors are unveiled to govern the properties of these P3HT-based BCPs: i) rigidity of the insulating coil, ii) energy levels between the constituent polymers, and iii) electrophilicity of the insulating coil. Accordingly, P3HT-b-P2VP-based photosynaptic transistor with a sought-after BCP combination demonstrates long-term memory behavior with current contrast up to 10 5 , short-term memory behavior with high paired-pulse facilitation ratio of 1.38, and an ultralow energy consumption of 0.56 fJ at an operating voltage of −0.0003 V. As far as it is known, this is the first work to utilize conjugated BCPs in an electret-free photosynaptic transistor showing great potential to the artificial intelligence technology.

show abstract

“…Unfortunately, this is hard to achieve by the Si and CMOS-based digital circuits. While many neuromorphic devices were developed based on organic semiconductors and quantum dots (QDs) and other emergence materials and new processing technology 18 – 20 . Moreover, the functioning mechanisms of these existing devices, including interface trap-induced neuromorphic behaviors, remained elusive and less controllable.…”

Section: Introductionmentioning

confidence: 99%

Neuromorphic device based on silicon nanosheets

Wang

et al. 2022

Nat Commun

Self Cite

View full text Add to dashboard Cite

Silicon is vital for its high abundance, vast production, and perfect compatibility with the well-established CMOS processing industry. Recently, artificially stacked layered 2D structures have gained tremendous attention via fine-tuning properties for electronic devices. This article presents neuromorphic devices based on silicon nanosheets that are chemically exfoliated and surface-modified, enabling self-assembly into hierarchical stacking structures. The device functionality can be switched between a unipolar memristor and a feasibly reset-able synaptic device. The memory function of the device is based on the charge storage in the partially oxidized SiNS stacks followed by the discharge activated by the electric field at the Au-Si Schottky interface, as verified in both experimental and theoretical means. This work further inspired elegant neuromorphic computation models for digit recognition and noise filtration. Ultimately, it brings silicon - the most established semiconductor - back to the forefront for next-generation computations.

show abstract

Silicon-based inorganic-organic hybrid optoelectronic synaptic devices simulating cross-modal learning

Cited by 23 publications

References 51 publications

Dual‐Modal Optoelectronic Synaptic Devices with Versatile Synaptic Plasticity

Dual‐Modal Optoelectronic Synaptic Devices with Versatile Synaptic Plasticity

Low‐Energy‐Consumption and Electret‐Free Photosynaptic Transistor Utilizing Poly(3‐hexylthiophene)‐Based Conjugated Block Copolymers

Neuromorphic device based on silicon nanosheets

Contact Info

Product

Resources

About