A bootstrapped analog switch with constant on-resistance is proposed for the successive approximation (SA) analog-to-digital converters (ADCs) that have many input-sampling switches. The initialization circuit, which is composed of a short pulse generator and a transmission gate, improves the linearity of the proposed bootstrapped analog switch by reducing the effect of the capacitive load. To evaluate the proposed bootstrapped analog switch, the 10-bit 1 MS/s CMOS SA ADC with a rail-to-rail differential input signal was designed by using a 0.18 µm CMOS process with 1.0 V supply voltage. The proposed bootstrapped analog switch reduced the maximum VGS variation of the conventional bootstrapped analog switch by 67%. It also enhanced the signal to noise-distortion ratio of the SA ADC by 4.8 dB when the capacitance of its gate node is 100 fF, and this improvement was maximized when the capacitance of its gate node increases.

A 200 kS/s 10-bit successive approximation (SA) analog-to-digital converter (ADC) with a rail-to-rail input signal is proposed for acquiring biosignals such as EEG and MEG signals. A split-capacitor-based digital-to-analog converter (SC-DAC) is used to reduce the power consumption and chip area. The SC-DAC's linearity is improved by using dummy capacitors and a small bootstrapped analog switch with a constant on-resistance, without increasing its area. A time-domain comparator with a replica circuit for clock feed-through noise compensation is designed by using a highly differential digital architecture involving a small area. Its area is about 50% less than that of a conventional time-domain comparator. The proposed SA ADC is implemented by using a 0.18-µm 1-poly 6-metal CMOS process with a 1 V supply. The measured DNL and INL are +0.44/-0.4 LSB and +0.71/-0.62 LSB, respectively. The SNDR is 55.43 dB for a 99.01 kHz analog input signal at a sampling rate of 200 kS/s. The power consumption and core area are 5 µW and 0.126 mm2, respectively. The FoM is 47 fJ/conversion-step.

As deep learning-based speech recognition systems are spotlighted, the need for large-scale speech databases for acoustic model training is increasing. Broadcast data can be easily used for database construction, since it contains transcripts for the hearing impaired. However, the subtitle timestamps have not been used to extract speech data because they are often inaccurate due to the inherent characteristics of closed captioning. Thus, we propose to build a large-scale speech database from multi-genre broadcast data with inaccurate subtitle timestamps. The proposed method first extracts the most likely speech intervals by removing subtitle texts with low subtitle quality index, concatenating adjacent subtitle texts into a merged subtitle text, and adding a margin to the timestamp of the merged subtitle text. Next, a speech recognizer is used to extract a hypothesis text of a speech segment corresponding to the merged subtitle text, and then the hypothesis text obtained from the decoder is recursively aligned with the merged subtitle text. Finally, the speech database is constructed by selecting the sub-parts of the merged subtitle text that match the hypothesis text. Our method successfully refines a large amount of broadcast data with inaccurate subtitle timestamps, taking about half of the time compared with the previous methods. Consequently, our method is useful for broadcast data processing, where bulk speech data can be collected every hour.