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Jaeseung Lim

Publications and source records attributed to Jaeseung Lim.

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Self-Focusing Control for Depth-Precise Wafer Slicing of 4H-SiC in Femtosecond Laser Processing

4H-SiC has emerged as a third-generation chip material because its superior thermal conductivity and high breakdown field enable the material to achieve high power density and higher switching frequencies in power-electronics applications. As chip architectures evolve toward 3D and heterogeneous integration, the mechanical and thermal design space tightens while yield risks grow. In particular, advanced packages require mid-process wafer thinning to < 100 $\mu$m to shorten interconnects and control thermo-mechanical stress. Femtosecond laser slicing for 4H-SiC wafers offers a non-contact processing approach to produce thin layers with low defects, while strong optical nonlinearities obscure the relationship between the laser parameters and the resulting slicing quality. Here, we systematically investigate Kerr-induced self-focusing using a femtosecond laser in 4H-SiC slicing by combining experiments, a semi-empirical analytical model, and numerical ray optics simulations. We demonstrate that the interplay between pulse energy and processing depth governs the self-focusing behavior, which directly correlates with post-separation surface texture parameters and separation stress, thereby linking nonlinear beam propagation to slicing quality. Based on this relationship, we define a processability map in the pulse energy with self-focusing depth space over a normalized irradiance background. Analytically, the model extends the Marburger formula to focused beams by replacing the power ratio with a normalized irradiance. Ray optics simulations capture the geometric features at the self-focusing point and are validated against experimental observations. Within physically defined thresholds, the processability map directly connects laser parameters to separation stress and surface texture metrics, providing practical guidance for depth control beyond trial-and-error.

physics.optics

Active Semi-Supervised Learning by Exploring Per-Sample Uncertainty and Consistency

Active Learning (AL) and Semi-supervised Learning are two techniques that have been studied to reduce the high cost of deep learning by using a small amount of labeled data and a large amount of unlabeled data. To improve the accuracy of models at a lower cost, we propose a method called Active Semi-supervised Learning (ASSL), which combines AL and SSL. To maximize the synergy between AL and SSL, we focused on the differences between ASSL and AL. ASSL involves more dynamic model updates than AL due to the use of unlabeled data in the training process, resulting in the temporal instability of the predicted probabilities of the unlabeled data. This makes it difficult to determine the true uncertainty of the unlabeled data in ASSL. To address this, we adopted techniques such as exponential moving average (EMA) and upper confidence bound (UCB) used in reinforcement learning. Additionally, we analyzed the effect of label noise on unsupervised learning by using weak and strong augmentation pairs to address datainconsistency. By considering both uncertainty and datainconsistency, we acquired data samples that were used in the proposed ASSL method. Our experiments showed that ASSL achieved about 5.3 times higher computational efficiency than SSL while achieving the same performance, and it outperformed the state-of-the-art AL method.

cs.CV

Pose-MUM : Reinforcing Key Points Relationship for Semi-Supervised Human Pose Estimation

A well-designed strong-weak augmentation strategy and the stable teacher to generate reliable pseudo labels are essential in the teacher-student framework of semi-supervised learning (SSL). Considering these in mind, to suit the semi-supervised human pose estimation (SSHPE) task, we propose a novel approach referred to as Pose-MUM that modifies Mix/UnMix (MUM) augmentation. Like MUM in the dense prediction task, the proposed Pose-MUM makes strong-weak augmentation for pose estimation and leads the network to learn the relationship between each human key point much better than the conventional methods by adding the mixing process in intermediate layers in a stochastic manner. In addition, we employ the exponential-moving-average-normalization (EMAN) teacher, which is stable and well-suited to the SSL framework and furthermore boosts the performance. Extensive experiments on MS-COCO dataset show the superiority of our proposed method by consistently improving the performance over the previous methods following SSHPE benchmark.

cs.CV