Degradation evaluation of poly-Si TFTs by comparing normal and reverse characteristics and behavior analysis of hot-carrier degradation

Kasakawa, Tomohiro; Tabata, Hitoshi; Onodera, Ryo; Kojima, Hirotsugu; Kimura, Mutsumi; Hara, Hiroyuki; Inoue, Satoshi

doi:10.1016/j.sse.2010.11.013

Cited by 8 publications

(1 citation statement)

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“…There are two modes for the characteristic degradations of low-temperature poly-Si thin-film transistors: the Joule-heating and hot-carrier degradations. 23,24) We investigated the characteristic degradations in detail, sorted the stress voltage conditions for the Joule-heating and hotcarrier degradations, which were systematically evaluated elsewhere, 25) and decided to utilize the Joule-heating degradation for the artificial neural networks. This is preferable because the characteristic degradations are symmetrical, 25) and electric currents flow in alternating directions through the synapse transistors.…”

Section: Neuron and Synapsementioning

confidence: 99%

Apoptotic self-organized electronic device using thin-film transistors for artificial neural networks with unsupervised learning functions

Kimura

Miyatani

Fujita

et al. 2015

Jpn. J. Appl. Phys.

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Artificial neural networks are promising systems for information processing with many advantages, such as self-teaching and parallel distributed computing. However, conventional networks consist of extremely intricate circuits to guarantee accurate behaviors of the neurons and synapses. We demonstrate an apoptotic self-organized electronic device using thin-film transistors for artificial neural networks with unsupervised learning functions. First, we formed a "neuron" from only eight transistors and reduced a "synapse" to only one transistor by employing the characteristic degradations of the synapse transistors to adjust the synaptic connection strength. Second, we classified the synapses into two types, "concordant" and "discordant" synapses, and composed a local interconnective network optimized for integrated electronic circuits. Finally, we confirmed that the device is feasible and can learn multiple logical operations, including AND, OR, and XOR.

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