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Laser spectroscopy of the^229mTh nuclear clock transition is necessary for the future construction of a nuclear-based optical clock. Precision laser sources with broad spectral coverage in the vacuum ultraviolet are needed for this task. Here, we present a tunable vacuum-ultraviolet frequency comb based on cavity-enhanced seventh-harmonic generation. Its tunable spectrum covers the current uncertainty range of the^229mTh nuclear clock transition.

Spiking Neural Networks (SNNs) are brain- inspired computing models incorporating unique temporal dynamics and event-driven processing. Rich dynamics in both space and time offer great challenges and opportunities for efficient processing of sparse spatiotemporal data compared with conventional artificial neural networks (ANNs). Specifically, the additional overheads for handling the added temporal dimension limit the computational capabilities of neuromorphic accelerators. Iterative processing at every time-point with sparse inputs in a temporally sequential manner not only degrades the utilization of the systolic array but also intensifies data movement.In this work, we propose a novel technique and architecture that significantly improve utilization and data movement while efficiently handling temporal sparsity of SNNs on systolic arrays. Unlike time-sequential processing in conventional SNN accelerators, we pack multiple time points into a single time window (TW) and process the computations induced by active synaptic inputs falling under several TWs in parallel, leading to the proposed parallel time batching. It allows weight reuse across multiple time points and enhances the utilization of the systolic array with reduced idling of processing elements, overcoming the irregularity of sparse firing activities. We optimize the granularity of time-domain processing, i.e., the TW size, which significantly impacts the data reuse and utilization.more »We further boost the utilization efficiency by simultaneously scheduling non-overlapping sparse spiking activities onto the array. The proposed architectures offer a unifying solution for general spiking neural networks with commonly exhibited temporal sparsity, a key challenge in hardware acceleration, delivering 248X energy-delay product (EDP) improvement on average compared to an SNN baseline for accelerating various networks. Compared to ANN based accelerators, our approach improves EDP by 47X on the CIFAR10 dataset.« less

Due to the growing complexity and numerous manufacturing variation in safety-critical analog and mixed-signal (AMS) circuit design, rare failure detection in the high-dimensional variational space is one of the major challenges in AMS verification. Efficient AMS failure detection is very demanding with limited samples on account of high simulation and manufacturing cost. In this work, we combine a reversible network and a gating architecture to identify essential features from datasets and reduce feature dimension for fast failure detection. While reversible residual networks (RevNets) have been actively studied for its restoration ability from output to input without the loss of information, the gating network facilitates the RevNet to aim at effective dimension reduction. We incorporate the proposed reversible gating architecture into Bayesian optimization (BO) framework to reduce the dimensionality of BO embedding important features clarified by gating fusion weights so that the failure points can be efficiently located. Furthermore, we propose a conditional density estimation of important and non-important features to extract high-dimensional original input features from the low-dimension important features, improving the efficiency of the proposed methods. The improvements of our proposed approach on rare failure detection is demonstrated in AMS data under the high-dimensional process variations.

Wafer map pattern recognition is instrumental for detecting systemic manufacturing process issues. However, high cost in labeling wafer patterns renders it impossible to leverage large amounts of valuable unlabeled data in conventional machine learning based wafer map pattern prediction. We proposed a contrastive learning framework for semi-supervised learning and prediction of wafer map patterns. Our framework incorporates an encoder to learn good representation for wafer maps in an unsupervised manner, and a supervised head to recognize wafer map patterns. In particular, contrastive learning is applied for the unsupervised encoder representation learning supported by augmented data generated by different transformations (views) of wafer maps. We identified a set of transformations to effectively generate similar variants of each original pattern. We further proposed a novel rotation-twist transformation to augment wafer map data by rotating each given wafer map for which the angle of rotation is a smooth function of the radius. Experimental results demonstrate that the proposed semi-supervised learning framework greatly improves recognition accuracy compared to traditional supervised methods, and the rotation-twist transformation further enhances the recognition accuracy in both semi-supervised and supervised tasks.