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    <title>Repository Collection: null</title>
    <link>https://scholar.dgist.ac.kr/handle/20.500.11750/12957</link>
    <description />
    <pubDate>Mon, 03 Aug 2026 10:08:07 GMT</pubDate>
    <dc:date>2026-08-03T10:08:07Z</dc:date>
    <item>
      <title>A Hybrid Digital-Analog Compute-in-Memory Using Content-Addressable Memory With Flexible Multi-Bit Slicing</title>
      <link>https://scholar.dgist.ac.kr/handle/20.500.11750/60479</link>
      <description>Title: A Hybrid Digital-Analog Compute-in-Memory Using Content-Addressable Memory With Flexible Multi-Bit Slicing
Author(s): Jung, Sangwoo; Lee, Hojin; Park, Jiyong; Lee, Yejin; Park, Dahoon; Shin, Hyunseob; Yoon, Jong-Hyeok; Kung, Jaeha
Abstract: Compute-in-memory (CIM) reduces data movement and enhances compute parallelism, making it suitable for AI applications. However, analog CIMs, yet energy-efficient, are vulnerable to PVT variations, while digital CIMs offer robustness but limited efficiency due to their bit-wise computation overhead. To address these challenges, we propose a hybrid CIM architecture that integrates content-addressable memory (CAM) and cluster-based CIM, named CAM-CIM, fabricated in 65nm CMOS technology. The proposed CAM-CIM flexibly slices multi-bit weights, assigning MSBs to CAM and LSBs to CIM, enabling dynamic accuracy-efficiency trade-offs across various bit precisions. A two-stage 8:3 compressor-based adder tree improves CAM efficiency and a reference voltage search algorithm ensures accurate CIM computation with low-bit ADCs. Our CAM-CIM supports 1-8b inputs/weights with reconfigurable compute modes, leveraging ternary-CAM based selective columns and cluster-wise CIM processing to produce multiple trade-off points even in the same bit precision. A prototype chip with a RISC-V controller and custom instructions is demonstrated that shows energy efficiencies of 32.4TOPS/W (8b/8b) and 76.0-354.9TOPS/W (4b/4b) with 0.66% accuracy loss, on average, across a wide range of DNN benchmarks including CNNs and vision transformers on CIFAR and ImageNet datasets.</description>
      <pubDate>Thu, 30 Apr 2026 15:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://scholar.dgist.ac.kr/handle/20.500.11750/60479</guid>
      <dc:date>2026-04-30T15:00:00Z</dc:date>
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    <item>
      <title>High-Spatiotemporal-Resolution Transparent Thermoelectric Temperature Sensor Arrays Reveal Temperature-Dependent Windows for Reversible Photothermal Neuromodulation</title>
      <link>https://scholar.dgist.ac.kr/handle/20.500.11750/59973</link>
      <description>Title: High-Spatiotemporal-Resolution Transparent Thermoelectric Temperature Sensor Arrays Reveal Temperature-Dependent Windows for Reversible Photothermal Neuromodulation
Author(s): Lee, Junhee; Yoon, Dongjo; Lee, Jungha; Kim, Duhee; Kim, Eunui; Yoon, Jong-Hyeok; Kwon, Hyuk-Jun; Chung, Seungjun; Nam, Yoonkey; Kang, Hongki
Abstract: Photothermal neural stimulation enables optical excitation or inhibition of neural activity depending on the dynamics of localized temperature changes, offering high spatial resolution without genetic modification. However, quantitative analysis of these temperature dynamics remains limited due to the lack of suitable direct sensing technologies, posing a challenge to the safe and controlled application of photothermal neural stimulation techniques. This challenge is addressed by developing transparent thermoelectric temperature sensor arrays with high spatiotemporal resolution, integrated with electrical and optical recording capabilities. These microscale sensors stably and accurately capture rapid temperature increases and decreases, and thermal equilibrium induced by thermo-plasmonic effects at the neural interface, regardless of the environment. The multifunctional platform allows simultaneous electrical and optical monitoring of neural responses during the photothermal stimulation, enabling detailed analysis of the correlation between localized temperature changes and neural activities. a reversible neural inhibition window (1.4-4.5 degrees C) and thresholds for irreversible damage (&gt;6.1 degrees C) are identifyed. Using high temporal-resolution sensing, localized thermo-plasmonic temperature dynamics over tens of milliseconds, and associated neural signal suppression and reactivation are captured. This approach provides unprecedented insight into the interplay between photothermal effects and neural activity, establishing a foundation for precise, temperature-guided neuromodulation therapies and advanced neural circuit research.</description>
      <pubDate>Sat, 31 Jan 2026 15:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://scholar.dgist.ac.kr/handle/20.500.11750/59973</guid>
      <dc:date>2026-01-31T15:00:00Z</dc:date>
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    <item>
      <title>Translational reprogramming of dentate gyrus peptidergic circuitry gates antidepressant efficacy</title>
      <link>https://scholar.dgist.ac.kr/handle/20.500.11750/59929</link>
      <description>Title: Translational reprogramming of dentate gyrus peptidergic circuitry gates antidepressant efficacy
Author(s): Oh, Seo-Jin; Jang, Jin-Hyeok; Roussarie, Jean-Pierre; Jang, Kyung-un; Jeong, Min-Seok; Jo, Yeon Suk; Shin, Chang Hun; Choi, Hongsoo; Lee, Kwang; Yoon, Jong-Hyeok; Oh, Yong-Seok
Abstract: Selective serotonin reuptake inhibitors (SSRIs) exhibit delayed therapeutic effects despite rapid serotonin elevation, suggesting their dependence on slow neuroplastic adaptations. Here, we demonstrate that antidepressant actions require cell type-specific translational regulation of the peptidergic signaling in the dentate gyrus (DG). Chronic, but not acute, treatment with an SSRI fluoxetine (FLX) selectively enhances translational activity in hilar mossy cells (MCs), with no detectable changes in neighboring granule cells (GCs). Combining Translating Ribosome Affinity Purification (TRAP) with RNA sequencing revealed distinct baseline translatomes between these two glutamatergic neurons and identified FLX-induced remodeling of peptidergic pathways in the DG. Crucially, we discovered MC-specific enrichment of the neuropeptide PACAP, which undergoes translation-dependent upregulation by chronic FLX treatment. This PACAP induction mediates neuroadaptive plasticity in PAC1 receptor-expressing GCs and drives behavioral responses prominently in female mice during prolonged FLX administration. Our findings establish cell type-specific translational reprogramming as a novel mechanistic framework for antidepressant action.</description>
      <pubDate>Sat, 31 Jan 2026 15:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://scholar.dgist.ac.kr/handle/20.500.11750/59929</guid>
      <dc:date>2026-01-31T15:00:00Z</dc:date>
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    <item>
      <title>Design and Analysis of ΔΣ Modulator Analogous Bang-Bang Digital PLL</title>
      <link>https://scholar.dgist.ac.kr/handle/20.500.11750/58567</link>
      <description>Title: Design and Analysis of ΔΣ Modulator Analogous Bang-Bang Digital PLL
Author(s): Park, Minsu; Yoon, Jong-Hyeok; Song, Minyoung
Abstract: This paper presents the analysis and design methodology of a second-order ΔΣ modulator analogous bang-bang digital phase-locked loop (DSBPLL). When the bang-bang-based digital PLL (BB-DPLL) cannot fully track the DCO jitter, the jitter slewing effect exacerbates the in-band noise. The proposed DSBPLL can increase the PLL filter order without using a high-order loop filter, thereby mitigating the in-band noise caused by input tracking jitter. Theoretical noise analysis confirmed that the proposed DSBPLL can reduce 54.3% of the integrated jitter from 100 kHz to 100 MHz, consistent with the measurement results.</description>
      <pubDate>Wed, 31 Dec 2025 15:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://scholar.dgist.ac.kr/handle/20.500.11750/58567</guid>
      <dc:date>2025-12-31T15:00:00Z</dc:date>
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