The decrease of feature size and the growing complexity of the fabrication process lead to more failures in manufacturing semiconductor devices. Therefore, identifying the root cause layout patterns of failures becomes increasingly crucial for yield improvement. In this article, a novel layout-aware diagnosis-based layout pattern analysis framework is proposed to identify the root cause efficiently. At the first stage of the framework, an encoder network trained using contrastive learning is used to extract representations of layout snippets that are invariant to trivial transformations, including shift, rotation, and mirroring, which are then clustered to form layout patterns. At the second stage, we model the causal relationship between any potential root cause layout patterns and the systematic defects by a structural causal model, which is then used to estimate the average causal effect (ACE) of candidate layout patterns on the systematic defect to identify the true root cause. Experimental results on real industrial cases demonstrate that our framework outperforms a commercial tool with higher accuracies and around$\times 8.4$speedup on average.

Identifying root causes of systematic defects is a crucial step in yield enhancement process of integrated circuit (IC) manufacturing. With increasing complexity of fabrication processes and decreasing sizes of pattern features, more systematic defects occur at advanced technology nodes, and traditional methods are unfeasible to directly identify failure causes, due to expensive time and labor costs. Root cause analysis (RCA) technology is thus studied to automatically identify common root causes in a short time. In this paper, we develop RCANet, an end-to-end unsupervised learning-based RCA framework, which analyses diagnosis reports of failing dies within a wafer and identifies both layout-aware and cell-internal root causes efficiently. Experimental results on designs with different technologies demonstrate that RCANet outperforms both a commercial tool and the state-of-the-art method.

The decrease of feature size and the growing complexity of the fabrication process lead to more failures in manufacturing semiconductor devices. Therefore, identifying the root cause layout patterns of failures becomes increasingly crucial for yield improvement. In this paper, a novel layout-aware diagnosis-based layout pattern analysis framework is proposed to identify the root cause efficiently. At the first stage of the framework, an encoder network trained using contrastive learning is used to extract representations of layout snippets that are invariant to trivial transformations including shift, rotation, and mirroring, which are then clustered to form layout patterns. At the second stage, we model the causal relationship between any potential root cause layout patterns and the systematic defects by a structural causal model, which is then used to estimate the Average Causal Effect (ACE) of candidate layout patterns on the systematic defect to identify the true root cause. Experimental results on real industrial cases demonstrate that our framework outperforms a commercial tool with higher accuracies and around x8.4 speedup on average.

Root Cause Analysis (RCA) is a critical technology for yield improvement in integrated circuit manufacture. Traditional RCA prefers unsupervised algorithms such as Expectation Maximization based on Bayesian models. However, these methods are severely limited by the weak predictive capability of statistical models and can’t effectively transfer the yield learning experience from old designs and processes to the new ones. Motivated by recent advancements of deep learning, in this paper we propose a Neural-Network-based adaptive framework for RCA in yield improvement. The proposed framework consists of an inference module and a self-adaptive module. The former receives volume diagnosis reports and predicts the root cause distributions. The latter is able to adapt the inference module to new designs and processes based on a few of targeted samples without any manual adjustment. Experimental results show that a relatively large improvement on accuracy is achieved by the proposed framework on simulated diagnosis data. Furthermore, the transferring capability of the self-adaptive module is also validated by the results.

In this paper, an automated tracking system with head and tail recognition for time-lapse observation of free-moving C. elegans is presented. In microscale field, active C. elegans can move out of the view easily without an automated tracking system because of the narrow field of view and rapid speed of C. elegans. In our previous works, we constructed an automated platform with 3D freedom to track centroid region of the nematode successfully. However, tracking time was not long enough to support a full time-lapse observation. Our proposed system in this study integrate the detection method in horizontal plane with depth evaluation more tightly. Tracking time and response speed have been greatly improved. Besides, we make full use of curvature calculation to make the system recognize the head and tail of C. elegans and the recognition rate can be up to 95%. The results demonstrate that the system can fully achieve automated long-term tracking of a free-living nematode and will be a nice tool for C. elegans behavioral analysis.

We have successfully achieved manipulation and assembly of microbeads having the size of 100μm diameter by hemispherical end-effectors with high stability and accuracy. The motivation of achieving assembly of actual cells lies in the great significance of it in tissue regeneration and cell analysis. Firstly, the most difficult problem we need to solve is the releasing problem caused by adhesion force. The viscosity on cell surface is much larger than the microbeads which makes cell releasing challenging. Secondly, the cell can generate its deformation, then contact area with end-effector will change during grasping process. This may influence the adhesion force and also bring problem to releasing. Thirdly, cell is much smaller, around 15μm in diameter, so we need to fabricate smaller end-effector to achieve successful manipulation and ensure the stability in the meantime. In this paper, we realize the manipulation by decreasing the adhesion forces and apply vibration to release a cell stably. We found the appropriate scale size for the end-effector is around 10μm diameter. It can not only grasp a 15μm cell but also bring little interference to the environment. As a demonstration of the proposed manipulation method, the repeated experiments were conducted to explore the dependence of adhesion force on the grasping distance, which can be helpful in the improvement of successful rate. Finally, we achieved automatic cell assembly using Hela cells.

Task planning plays a vital role in the application of the earth observation satellites (EOS) as it can effectively reduce the task execution delay and the system energy consumption. However, it is a classic NP-hard problem. In this paper, we propose a graph-based scheduling algorithm to match the to-be-observed tasks with the sensor resources for the multi-satellite multi-task scenario. Specially, we construct a collision avoidance clustering graph (CACG) to model the relationship between tasks and resources, where the concept of collision set is creatively constructed to characterize the conflict relationships among tasks. Through analyzing which nodes can be a clique in the graph, we can get the aggregated tasks. As such a CACG-based algorithm is proposed to achieve satellite task scheduling, where three criteria,i.e.,the N-priority criterion, the T-priority criterion and the collision avoidance rule, are proposed to enhance the percentage of the completed task and decrease the delays. Finally, we demonstrate the effectiveness of the proposed schemes through simulations.

Nowadays single cell analysis becomes a more and more important method to gather the information of individual cells and study the heterogeneity of cells caused by random expression of gene, protein and the level of metabolism. Many device and technologies of single cell analysis have been developed to meet these needs. In this paper, we presented a high-throughput microchannel for single cell immobilization with small sheer pressure. It features high density arrays, which can accommodate up to 130~300 traps within 1~2 mm2. According to our experiment, about 91% of capture unit can be occupied by the single cell in 40 s, using the optimized structure of microchannels. Therefore, we expect that the high throughput microchannels can be of great importance for the biological research.