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To accurately determine the reliability of SRAMs, we propose a method to estimate the wearout parameters of FEOL TDDB using on-line data collected during operations. Errors in estimating lifetime model parameters are determined as a function of time, which are based on the available failure sample size. Systematic errors are also computed due to uncertainty in estimation of temperature and supply voltage during operations, as well as uncertainty in process parameters and use conditions. 

Accelerated lifetime tests are necessary for reliability evaluation of circuits and systems, but the parameters for choosing the test conditions are often unknown. Furthermore, reliability testing is generally performed on test structures that have different properties than actual circuits and systems, which may create inconsistencies in how circuits and systems work in reality. To combat this problem, we use ring oscillators, which are similar to circuits, based on the 14nm FinFET node as the circuit vehicle to extract wearout data. We explore the effects of testing time, sample size, and number of stages on the ability to detect failures for various test conditions, focusing on front-end time dependent dielectric breakdown, which is one of the most dominant wearout mechanisms. 

Circuits may fail in the field due to a wide variety of failure modes. If there are frequent failures in the field, circuits are returned to the manufacturer, and the causes of failure must be identified. The challenge is that wearout mechanisms are confounded in circuit and system-level failure data. Using such failure data, it is often hard to separate the underlying failure causes without time-consuming and expensive physical failure analysis. To distinguish the wearout mechanisms for each failure sample, we have developed a quick and low-cost methodology using maximum likelihood estimation and probability analysis to determine the origin of the failure distributions, region of error, and sorting accuracy. We apply our methodology to analyze the competing wearout mechanisms in 14nm FinFET ring oscillators, as an example, using simulation. We also consider the problem of Trojan detection. 

Electroencephalography (EEG)-based emotion classification has drawn increasing attention yet EEG signals associated with emotional responses are still elusive. This study applies a multi-model adaptive mixture independent component analysis (AMICA) as an unsupervised approach to identify and characterize emotional states. Empirical results showed that the AMICA was able to learn distinct models that accounted for four self-imagery emotions. While large-scale analyses and careful examinations are needed, the pilot study offers evidence for AMICA as a promising, data-driven approach to model EEG dynamics of self-imagery emotions. 

Abstract We report the observation of gravitational waves from two binary black hole coalescences during the fourth observing run of the LIGO–Virgo–KAGRA detector network, GW241011 and GW241110. The sources of these two signals are characterized by rapid and precisely measured primary spins, nonnegligible spin–orbit misalignment, and unequal mass ratios between their constituent black holes. These properties are characteristic of binaries in which the more massive object was itself formed from a previous binary black hole merger and suggest that the sources of GW241011 and GW241110 may have formed in dense stellar environments in which repeated mergers can take place. As the third-loudest gravitational-wave event published to date, with a median network signal-to-noise ratio of 36.0, GW241011 furthermore yields stringent constraints on the Kerr nature of black holes, the multipolar structure of gravitational-wave generation, and the existence of ultralight bosons within the mass range 10⁻¹³–10⁻¹²eV. 

Abstract On 2023 November 23, the two LIGO observatories both detected GW231123, a gravitational-wave signal consistent with the merger of two black holes with masses $137_{-18}^{+23}{M}_{\odot }$and $101_{-50}^{+22}{M}_{\odot }$(90% credible intervals), at a luminosity distance of 0.7–4.1 Gpc, a redshift of $0.40_{-0.25}^{+0.27}$, and with a network signal-to-noise ratio of ∼20.7. Both black holes exhibit high spins— $0.90_{-0.19}^{+0.10}$and $0.80_{-0.52}^{+0.20}$, respectively. A massive black hole remnant is supported by an independent ringdown analysis. Some properties of GW231123 are subject to large systematic uncertainties, as indicated by differences in the inferred parameters between signal models. The primary black hole lies within or above the theorized mass gap where black holes between 60–130M_⊙should be rare, due to pair-instability mechanisms, while the secondary spans the gap. The observation of GW231123 therefore suggests the formation of black holes from channels beyond standard stellar collapse and that intermediate-mass black holes of mass ∼200M_⊙form through gravitational-wave-driven mergers. 

The gravitational-wave signal GW250114 was observed by the two LIGO detectors with a network matched-filter signal-to-noise ratio of 80. The signal was emitted by the coalescence of two black holes with near-equal masses $m_1={33.6}_{-0.8}^{+1.2}{M}_{\odot }$and $m_2={32.2}_{-1.3}^{+0.8}{M}_{\odot }$, and small spins ${\chi }_{1,2}\le 0.26$(90% credibility) and negligible eccentricity $e\le 0.03$. Postmerger data excluding the peak region are consistent with the dominant quadrupolar $(\ell =|m|=2)$mode of a Kerr black hole and its first overtone. We constrain the modes’ frequencies to $\pm 30\%$of the Kerr spectrum, providing a test of the remnant’s Kerr nature. We also examine Hawking’s area law, also known as the second law of black hole mechanics, which states that the total area of the black hole event horizons cannot decrease with time. A range of analyses that exclude up to five of the strongest merger cycles confirm that the remnant area is larger than the sum of the initial areas to high credibility.