L-shaped tunnel field-effect transistor (TFET) provides higher on-current than a conventional TFET through band-to-band tunneling in the vertical direction of the channel. However, L-shaped TFET is disadvantageous for low-power applications because of increased off-current due to the large ambipolar current. In this paper, a stacked gate L-shaped TFET is proposed for suppression of ambipolar current. Stacked gates can be easily implemented using the structural features of L-shaped TFET, and on- and off-current can be controlled separately by using different gates located near the source and the drain, respectively. As a result, the suppression of ambipolarity is observed with respect to work function difference between two gates by simulation of the band-to-band tunneling generation. Furthermore, the proposed device suppresses ambipolar current better than existing ambipolar current suppression methods. In particular, low drain resistance is achieved as there is no need to reduce drain doping, which leads to a 7% enhanced on-current. Finally, we present the fabrication method for a stacked gate L-shaped TFET.

To understand the underperformance of actively managed equity funds in Korean markets, we examine whether fund managers hold relatively over-priced stocks. To this end, we combine the information associated with 10 well-known anomalies to construct the implied mispricing measure for each stock and define it as the stock's A-score. On a fund level, we then construct the value-weighted average of the A-score decile ranks of individual stocks and define it as the investing measure. A fund with a high (low) investing measure means that it holds relatively undervalued (overvalued) stocks. Our empirical findings are as follows. First, on average, active funds do not hold undervalued stocks compared to benchmarks. Second, despite the underperformance of active funds, a subset of such funds persistently holds undervalued stocks. Third, our investing measure has a strong forecasting power regarding future performance. Funds with the highest 20% of the investing measure outperform those with the lowest 20% by an annualized four-factor alpha of 3.24%, which is statistically significant. Overall, our results suggest that the performance of active funds may be improved by exploiting stock market anomalies. Score를 이용하여 개별 펀드의 지표를 정의하였으며 이를 펀드의 Investing measure로 정의하였다. Investing measure가 높은(낮은) 펀드는 상대적으로 저평가(고평가)된 주식을 많이 보유하고 있음을 의미한다. 본 연구의 실증분석 결과는 다음과 같다. 첫째, 전체적으로 액티브펀드는 벤치마크에 비해 저평가된 주식을 보유하고 있지 않으며 이는 액티브펀드의 우월하지 못한 성과와 관련이 있는 것으로 해석된다. 둘째, 그럼에도 불구하고 일부의 액티브펀드는 벤치마크에 비해 지속적으로 저평가된 종목을 보유하고 있으며 상대적으로 우수한 성과를 달성하고 있음이 확인되었다. 셋째, Investing measure는 미래 액티브펀드의 성과에 대한 예측력을 보유하는 지표임을 확인하였다. Investing measure가 가장 높은 20%의 펀드 그룹은 그 지표가 가장 낮은 그룹에 비해 CAPM 알파는 월 0.33%(연 3.96%), 그리고 4요인 알파는 월 0.27%(연 3.24%) 높게 추정되었으며 이는 통계적으로 유의하다. 본 연구의 결과는 주식시장의 이상현상을 활용한 투자전략을 구사할 경우 액티브펀드의 성과가 개선될 수 있음을 시사 한다.

As the computing paradigm has shifted toward edge computing, improving the security of edge devices is attracting significant attention. However, because edge devices have limited resources in terms of power and area, it is difficult to apply a conventional cryptography system to protect them. On the other hand, as a simple security application, a physical unclonable function (PUF) can be implemented without power and area problems because it provides a security key by utilizing process variations without additional external circuits. Ferroelectric tunnel junctions (FTJs) are 2-terminal devices that store information by changing the resistance of a ferroelectric material, where the resistance is determined by the polarization states of the ferroelectric domains. Because polycrystalline ferroelectric materials have a multi-domain nature, domain variation can also be used as a randomness source to induce cell-to-cell variations along with process variations. In this paper, we demonstrate PUF operations of a low-power, small area 16 × 16 hafnium oxide (pure-HfO x )-based FTJ array using certain metrics. It is clear that the proposed array consisting of scaled FTJs has adequate randomness for security applications such that the array-level PUF operations are robust against model-based machine learning attacks.

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