三星电子

Zoned Namespace (ZNS) SSD is an emerging storage solution that reduces device-level garbage collection in conventional SSDs with a block interface. It reduces write amplification and provides more stable performance. Meanwhile, in-storage compression has been studied to improve storage efficiency in a host-transparent way, without imposing additional host overhead. However, the sequential write constraint of ZNS SSDs poses a challenge to adopting in-storage compression, as data placement rules prevent variable-length compressed data from being packed into designated chunks. When the compressed data size exceeds the chunk size, the data must be split across multiple chunks. Such splits lead to internal fragmentation and performance degradation due to double reads, in which multiple physical pages must be read for a single logical data request.In this paper, we propose Braid-ZNS, a novel in-storage compression framework for ZNS SSDs. Braid-ZNS leverages the Zone Random Write Area (ZRWA) by temporarily allowing in-place updates, which are normally prohibited in ZNS zones. This capability enables the system to reorganize compressed blocks within the ZRWA in a size-aware manner, preventing a single logical page from being split across multiple fragments. Through this reorganization, Braid-ZNS eliminates double reads by ensuring that each logical page can be retrieved with a single access. Our evaluation demonstrates that Braid-ZNS can achieve up to 62.9% capacity reduction and improve throughput by up to 2.24× compared to the state-of-the-art in-storage compression on ZNS SSDs.

In this study, an operation-dependent self-adaptive threshold method was developed for steady-state identification of a residential air-conditioner. Conventional steady-state detectors (SSDs) generally use fixed manual thresholds determined under limited design conditions. However, even when the system remains in steady-state, the natural fluctuation of measured variables can vary with operating conditions. To address this limitation, steady-state fluctuations of seven characteristic variables were extracted from continuous no-fault operation data under 14 operating conditions. For each characteristic variable and operating condition, the steady-state threshold was defined as three times the standard deviation of the extracted steady-state data. The resulting condition-specific thresholds were then modeled as functions of indoor dry-bulb temperature, indoor relative humidity, and outdoor dry-bulb temperature using a regression-based adaptive threshold model. The proposed framework enables steady-state thresholds to be generated according to operating conditions, thereby providing a data-driven basis for automatic threshold generation within the tested operating range. In the reference steady-state datasets, the adaptive threshold increased the accepted steady-state moving-window samples from 63.1% to 98.3%, and from 26.6% to 96.2% in the thermostatic expansion valve (TXV) hunting region. In addition, the steady-state data identified using the proposed adaptive threshold framework were applied to no-fault reference modeling to examine their suitability as preprocessing data for subsequent fault detection and diagnosis applications.

Clustered semiconductor fabrication (CFAB) has recently been introduced in South Korea, connecting multiple FABs to meet the high demands of large-scale integration chip products. To make these FABs connected effectively, CFAB utilizes various types of interface modes (IMs) such as lifters and conveyors to transfer wafers between FABs. However, due to the operational algorithm limitations of IMs and the frequent wafer transfers between FABs to meet production requirements, traffic congestion among IMs often occurs unexpectedly. These instances of congestion can cause unpredictable traffic conditions in other IMs, making it more challenging to manage traffic in a CFAB. In this paper, we propose three analysis methods based on data mining and graph network analysis approaches to explore reciprocal relationships between IMs regarding traffic congestion and describe the reasons behind traffic congestion in each of them. To demonstrate the effectiveness of the proposed method, we conduct numerical experiments using a dataset collected from the real-world CFAB in South Korea. The findings through experiment results suggest that most traffic congestion occurs in only a few specific IMs in the CFAB, indicating that managing these IMs can reduce overall traffic congestion. Moreover, we provide visual insights into managerial implications to support decision-making for enhancing IM operation performance and mitigating traffic congestion bottlenecks in complex CFAB environments.

We present a high-precision measurement procedure for lens mold asphericity with high dynamic range, incorporating a novel calibration strategy based on radial shearing interferometry (RSI). The RSI system was first modeled using ZEMAX® ray-tracing software with a bulk-optics configuration and experimentally validated. A numerical approach, referred to as iterative reverse optimization, was introduced to adapt the initial lens design model to measured wavefront data. In this study, RSI was employed to rapidly minimize measurement departures, enabling calibration with a spherical target using multiple axial displacements at non-confocal positions combined with reverse optimization. The ZEMAX® model was optimized to simultaneously fit all non-confocal experimental wavefront departures, thereby revealing complete system parameters, including assembly deviations and misalignments. The calibrated system was subsequently applied to measure the asphericity of a lens mold, with the aspheric surface parameters retrieved through the proposed reverse optimization procedure. The extracted asphericity showed excellent agreement with independent measurements performed by the Form Talysurf contact method. This approach demonstrates a flexible and practical route for measuring asphericities of varying degrees in non-null testing conditions.

Large language model (LLM) agents rely on long-term memory to support extended interactions and personalized assistance beyond finite context windows. Existing memory agents actively update external memory through generated write, revise, and delete operations, but these updates may omit important information, corrupt existing memory, or introduce unsupported hallucinated content. Once stored, such errors become persistent system-state failures that can affect future reasoning and generation. In this paper, we propose TrustMem, a framework designed to improve the trustworthiness of memory consolidation. TrustMem relies on a Memory Transition Verifier to evaluate the transition process of memory updates in terms of coverage, preservation, and faithfulness. It further constructs preference pairs among candidate updates under the same memory state, enabling preference-guided reinforcement learning to directly optimize memory updating behaviors. Extensive experiments demonstrate that TrustMem improves both memory utility and reliability: it achieves state-of-the-art results across MemoryAgentBench, HaluMem, and the Mem-alpha validation set, improves HaluMem memory extraction by 12.14 F1 points, and reduces transition-level omission, corruption, and hallucination by 40.1%, 79.1%, and 50.0%, respectively, compared with the strongest baseline for each error type.