As the integration density on-chip increases, thermal challenges become more prominent, and the key to addressing these challenges lies in effective and efficient thermal analysis. Current thermal estimation methods face a fundamental paradigm limitation: they rely on single data source that captures only partial aspects of complex thermal behavior. Performance counter-based methods capture computational activity but lacks of accurate thermal readouts, while sensor-based approaches provide local temperature measurements but lack of comprehensive spatial coverage. This single-source paradigm has created an insurmountable accuracy ceiling in thermal map estimation, limiting the effectiveness of modern thermal management systems. We introduce the first multimodal data fusion framework for full-chip thermal estimation, leveraging denoising diffusion models to synergistically combine performance counters and thermal sensors. Our approach treats thermal mapping as a conditional generation problem, where complementary data modalities guide the reconstruction process through progressive denoising. The key insight is that thermal behavior is inherently multimodal-requiring both activity context (performance counters) and temperature ground truth (thermal sensors) for accurate estimation. Our multimodal fusion delivers transformative results: $90 \%+$ improvement over single-source methods (0.347 vs 4.862-6.302 average RMSE), 41.8% average RMSE improvement over state-of-the-art approaches, and robust performance across diverse sensor configurations (9-25 sensors). Furthermore, our framework effectively captures hotspot locations, with errors typically within 0.5 K. More importantly, this work establishes multimodal data fusion as a new paradigm for thermal analysis, opening new research directions and enabling next-generation thermal management systems with unprecedented accuracy and reliability, fundamentally changing how we approach thermal analysis in modern processors.

In this paper, we investigate an integrated sensing and communication (ISAC) system assisted by a dynamic metasurface antenna (DMA), where the base station (BS) simultaneously communicates with multiple users and performs target sensing. Specifically, this paper aims to maximize the radar signal-to-noise ratio (SNR) at the BS by jointly optimizing the beamforming matrix and the DMA weight matrix, subject to signal-to-interference-plus-noise ratio (SINR) constraints for the users, the maximum transmit power at the BS and the Lorentzian constraint associated with the DMA elements. To tackle this non-convex optimization problem, an alternating optimization (AO) algorithm is proposed. In this algorithm, semidefinite relaxation (SDR) is employed to optimize the beamforming matrix, while sequential rank-one constraint relaxation (SRCR) is used to optimize the DMA weight matrix. Additionally, the penalty dual decomposition (PDD) and successive convex approximation (SCA) techniques are utilized as an alternative approach to solve the DMA weight matrix. Simulation results demonstrate that the DMA-assisted ISAC system achieves favorable results. The impact of different parameters on the objective value is analyzed, which shows that the PDD method outperforms the SRCR method.

In the post-Moore era, three-dimensional integrated circuit (3D-IC) technology is a key direction for continuing to enhance chip performance. However, in the thermal simulation field, existing works either only address two-dimensional temperature fields or require a large number of samples and a long training time to train the model. In order to meet the current demand in chip design for rapid and accurate thermal prediction with limited sample sizes, this paper introduces a multi-fidelity model, T-Fusion, which combines tensor arithmetic and Bayesian autoregression. Leveraging a sparse set of high-fidelity data alongside abundant low-fidelity samples, T-Fusion reliably estimates high-fidelity thermal distribution across the chip. We validate our model on single-core double-layer chips, quad-core triple-layer, and octa-core double-layer chips respectively. We compare the predicted heat distribution with commercial thermal simulation software such as COMSOL, MTA, and Hotspot, achieving accelerations of 10,000x to 1,000,000x. T-fusion can also be applied to transient temperature prediction of the chip, requiring only 20 sets of high-precision data and 64 sets of low-precision data to control ME under 1K.

CMOS scaling faces challenges due to lithography and device physics issues, leading to increased costs and difficulties in expanding chip footprint. 3D integration technology offers increased integration density without increasing footprint, but elevated power density makes heat dissipation a significant challenge. Microchannel cooling effectively removes heat inside 3D chips. Traditional microchannel optimizations typically focus only on minimizing pump power within a limited parameter design space, leading to suboptimal cooling efficiency. Moreover, existing research rarely considers manufacturing costs, limiting practical application. To address these issues, we propose a high-dimensional non-uniform microchannel design scheme based on Segmented Sampling Bayesian Optimization (SSBO). This multi-parameter collaborative optimization framework comprehensively optimizes microchannel design. Our method reduces pump power by 70% compared to limited parameter design spaces. Additionally, we introduce a cost model for microchannel design, formulating a multi-objective optimization problem that considers both manufacturing cost and pump power consumption. By solving the multi-objective optimization problem by searching for the Pareto front, we demonstrate a balanced design between microchannel manufacturing cost and pump power and provide guidelines for key design parameters.

Static Random-Access Memory (SRAM) yield analysis is essential for semiconductor innovation, yet research progress faces a critical challenge: the large gap between simplified academic models and the complexities observed in practice. The lack of open, higher-fidelity benchmarks has hindered reproducibility and transferability, as promising academic techniques often fail to carry over to more realistic settings. We present OpenYield, an open-source ecosystem that aims to narrow this gap through three contributions: (i) An SRAM circuit generator that explicitly incorporates second-order effects (interconnect/line parasitics, inter-cell leakage coupling, and peripheralcircuit variations) that are commonly omitted in academic studies. (ii) A standardized evaluation platform with a simple interface and baseline yield-analysis implementations to enable fair comparisons and reproducible research on these higherfidelity circuits. (iii) An optimization platform for transistor-level sizing under these models, supporting reproducible studies of robustness/efficiency trade-offs. OpenYield aims to foster more reproducible and transferable progress in SRAM-yield research. The framework is publicly available at OpenYield:URL.

In this contribution, we analyze the asymptotic spectral-efficiency (ASE) of multiuser MIMO-CDMA systems, when assuming communications over flat fading channels with arbitrary spatial correlation. Our analysis is built on the operator-valued free probability theory, which is applied to obtain the limit distribution of the correlation matrix's eigenvalues, as the MIMO-CDMA systems' size tends to infinity. The spectral-efficiency (SE) performance of the MIMO-CDMA systems is investigated via both analysis and simulations. Our simulation and numerical results show that the ASE is capable of providing a good measure of the SE achieved by the corresponding realistic MIMO-CDMA systems.