Browsing by Author "Chiu, Li-Wen"

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3D Ta/TaOx/TiO2/Ti synaptic array and linearity tuning of weight update for hardware neural network applications
(2016-09-09)
Wang, I-Ting
;
Chang, Chih-Cheng
;
Chiu, Li-Wen
;
Chou, Teyuh
;
Hou, Tuo-Hung
The implementation of highly anticipated hardware neural networks (HNNs) hinges largely on the successful development of a low-power, high-density, and reliable analog electronic synaptic array. In this study, we demonstrate a two-layer Ta/TaOx/TiO2/Ti cross-point synaptic array that emulates the high-density three-dimensional network architecture of human brains. Excellent uniformity and reproducibility among intralayer and interlayer cells were realized. Moreover, at least 50 analog synaptic weight states could be precisely controlled with minimal drifting during a cycling endurance test of 5000 training pulses at an operating voltage of 3 V. We also propose a new state-independent bipolar-pulse-training scheme to improve the linearity of weight updates. The improved linearity considerably enhances the fault tolerance of HNNs, thus improving the training accuracy.
Development of Three-Dimensional Synaptic Device and Neuromorphic Computing Hardware
(2016-01-01)
Wang, I-Ting
;
Chou, Teyuh
;
Chiu, Li-Wen
;
Chang, Chih-Cheng
;
Hou, Tuo-Hung
To facilitate the development of low-cost, low-power, and high-density hardware neural networks, we have successfully developed a Ta/TaOx/TiO2/Ti RRAM-based synaptic device. The device exhibits numerous synaptic functions resembling those in biological synapses, including synaptic plasticity of potentiation and depression, spike-timing dependent plasticity, paired-pulse facilitation and a transition from short-term to long-term memory. We further demonstrate 3D high-density, high-connectivity integration of the Ta/TaOx/TiO2/Ti device, and the device exhibits excellent uniformity among interlayer and intralayer cells in a 4 x 4 3D two-layer cross-point array. Finally, we investigate the influence of nonlinearity of synaptic weight updates on neuromorphic computing. A state-independent training scheme is proposed to improve linearity and fault tolerance of training accuracy.