Browsing by Author "Lin, You-Yu"

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Pattern wiggling investigation of self-aligned double patterning for 2x nm node NAND Flash and beyond
(2013-01-01)
Lin, You-Yu
;
Chen, Chun-Chi
;
Li, Chia-Yu
;
Wang, Zih-Song
;
Chen, Ching-Hua
Double patterning technology (DPT) has been identified as the extension of optical photolithography technologies to 3x nm half-pitch and below to fill in the gap between Immersion and EUV lithography. Self-aligned double patterning (SADP) technology utilized mature process to reduce risk and faster time to support the continuation of Moore's Law. As for the SADP process, the suitable hard mask (HM) material as following core pattern selection is quite important. Usually, the severe pattern deformation -wiggling, is easy to happen as the line/space patterns scaled down to below 35nm, and it ultimately prevents the successful pattern transfer. In this paper, using the amorphous carbon as HM, it was found that wiggling was caused by serious chemical side-etch during SADP dry etch process. However, an effective of advanced carbon material with high etch selectivity and low etch rate by appropriate film modification can be successful in SADP without wiggling side effect for 2x nm node NAND Flash application. This extraordinary HM can be considered as a potential choice for SADP process continual performing.
Phone Boundary Detection Using Sample-Based Acoustic Parameters
(2010)
Lin, You-Yu
;
Wang, Yih-Ru
;
Liao, Yuan-Fu
;
傳播研究所
;
Institute of Communication Studies
A sample-based phone boundary detection algorithm is proposed in this paper. Some sample-based acoustic parameters are first extracted in the proposed method, including six sub-band signal envelopes, sample-based KL distance and spectral entropy. Then, the sample-based KL distance is used for boundary candidates pre-selection. Last, a supervised neural network is employed for final boundary detection. Experimental results using the TIMIT speech corpus showed that EERs of 13.2% and 15.1% were achieved for the training and test data sets, respectively. Moreover, 43.5% and 88.2% of boundaries detected were within 80- and 240-sample error tolerance from manual labeling results at the EER operating point.