GeSn-on-GaAs with photoconductive carrier lifetime &gt;400 ns: role of substrate orientation and atomistic simulation

Karthikeyan, Sengunthar; Johnston, Steven W.; Gayakwad, Dhammapriy; Mahapatra, Suddhasatta; Bodnar, Robert J.; Zhao, Jing; Joshi, Rutwik; Hudait, Mantu K.

doi:10.1039/d3nr05904a

Nanoscale

2024

DOI: 10.1039/d3nr05904a

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GeSn-on-GaAs with photoconductive carrier lifetime >400 ns: role of substrate orientation and atomistic simulation

Sengunthar Karthikeyan,

Steven W. Johnston,

Dhammapriy Gayakwad

et al.

Abstract: Group IV GeSn quantum material finds application in electronics and silicon-compatible photonics. Synthesizing these materials with low defect density and high carrier lifetime is a potential challenge due to lattice...

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A Self‐Driven Ga₂O₃ Memristor Synapse for Humanoid Robot Learning

Zhang,

Li,

et al. 2024

Small Methods

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In recent years, the rapid development of brain‐inspired neuromorphic systems has created an imperative demand for artificial photonic synapses that operate with low power consumption. In this study, a self‐driven memristor synapse based on gallium oxide (Ga2O3) nanowires is proposed and demonstrated successfully. This memristor synapse is capable of emulating a range of functionalities of biological synapses when exposed to 255 nm light stimulation. These functionalities encompass peak time‐dependent plasticity, pulse facilitation, and memory learning capabilities. It exhibits an ultrahigh paired‐pulse facilitation index of 158, indicating exceptional learning performance. The transition from short‐term memory to long‐term memory can be attributed to the remarkable relearning capabilities. Furthermore, the potential applications of the memristor synapse is showcased through the successful manipulation of a humanoid intelligent robot. Upon establishing artificial intelligence (AI) systems, the control commands originating from the synaptic device can drive the humanoid robot to perform various actions. Based on the memristor synapses, the autonomous feedback system of the humanoid robot facilitates a good collaboration between robotic actions and bio‐inspired light perception. Therefore, this research opens up an effective way to advance the development of neuromorphic computing technologies, AI systems, and intelligent robots that demand ultra‐low energy consumption.

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A Self‐Driven Ga₂O₃ Memristor Synapse for Humanoid Robot Learning

Zhang,

Li,

et al. 2024

Small Methods

View full text Add to dashboard Cite

show abstract

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GeSn-on-GaAs with photoconductive carrier lifetime >400 ns: role of substrate orientation and atomistic simulation

Abstract: Group IV GeSn quantum material finds application in electronics and silicon-compatible photonics. Synthesizing these materials with low defect density and high carrier lifetime is a potential challenge due to lattice...

Cited by 1 publication

References 61 publications

A Self‐Driven Ga₂O₃ Memristor Synapse for Humanoid Robot Learning

A Self‐Driven Ga₂O₃ Memristor Synapse for Humanoid Robot Learning

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GeSn-on-GaAs with photoconductive carrier lifetime >400 ns: role of substrate orientation and atomistic simulation

Abstract: Group IV GeSn quantum material finds application in electronics and silicon-compatible photonics. Synthesizing these materials with low defect density and high carrier lifetime is a potential challenge due to lattice...

Cited by 1 publication

References 61 publications

A Self‐Driven Ga2O3 Memristor Synapse for Humanoid Robot Learning

A Self‐Driven Ga2O3 Memristor Synapse for Humanoid Robot Learning

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Product

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A Self‐Driven Ga₂O₃ Memristor Synapse for Humanoid Robot Learning

A Self‐Driven Ga₂O₃ Memristor Synapse for Humanoid Robot Learning