The chase for high yield designs can effectively reduce chip manufacturing costs, making yield optimization a crucial problem in the IC community. However, the timeconsuming Monte Carlo simulations required by yield analysis hinder the traditional yield optimization methods from applying to the efficient analog circuit design flow, especially in high-dimensional design spaces. In this paper, we propose a high-dimensional yield optimization method via Monte Carlo tree search and self-regressive auto-encoder promoted subspace decomposed Gaussian process. To mitigate the common issue of over-exploration in high-dimensional optimization, a Monte Carlo tree is adopted to quickly identify promising local regions within the design space. The optimization in the selected region is realized by Bayesian optimization. To further accelerate the convergence speed of local optimization, a self-regressive autoencoder is proposed to adaptively learn for each performance metric an embedded linear subspace with dimensionality much smaller than the original design space. A specific subspace decomposed Gaussian process is constructed to model the yield variation based on the low-dimensional features of observed design parameters. A message passing algorithm is used to efficiently maximize the acquisition function, which obtains the next candidate design in intersected subspaces. Compared with the state-of-the-art methods, the proposed method achieves 2:20× – 3:99× speedup in simulation cost and 5:22×–14:33× speedup in time cost when tested in three real circuit designs.

With the emergence, development, and maturity of various Internet technologies, the healthcare industry is also trying to integrate with these technologies to provide more convenient healthcare services to patients by establishing telemedicine to develop and continuously improve the public healthcare system. Various protocols were proposed in the recent past to provide attack resistance in addition to computational efficiency; however, several such protocols were proved inefficient or prone to one or more attacks. Some of these protocols lack user anonymity and privacy. Keeping in consideration the flaws of existing protocols, in this article, we propose an efficient and secure protocol based on extremely lightweight symmetric key operations. In addition, we provide formal and informal analyses to demonstrate the protocol’s security. Moreover, to measure the efficiency of the proposed protocol, we conducted a real-time experiment, which confirms that the proposed protocol completes an authentication round in 117.1071 ms with an exchange of four messages and 2592 bits among the three participating entities. Finally, by comparing it with other protocols, we show that the proposed protocol has significant security advantages and performance benefits.

In the ever-evolving landscape of technology, healthcare continuously harnesses its benefits, propelling advancements in medical practices. Within intelligent healthcare, medical robots play a pivotal role, providing integral support to healthcare professionals, streamlining processes, and delivering efficient services. These robots securely transmit patient treatment plans, transferring them to cloud storage and subsequently storing them in blockchain systems. This innovative approach ensures the integrity and accessibility of patient data, introducing novel avenues for seamless interaction with medical information for hospitals and patients’ families. Despite these advantages, the looming privacy risks associated with sensitive patient data transmission pose a compelling challenge, demanding a comprehensive solution. In response to this challenge, we propose a mutual authentication and key agreement protocol designed to optimize healthcare services while prioritizing data security and patient privacy. To validate the robustness of our authentication protocol, we conduct thorough analyses based on both formal and informal models, establishing a foundational framework for evaluating the protocol’s security. Additionally, we perform a comprehensive comparative analysis, assessing the proposed protocol against existing counterparts across various dimensions. This comparative scrutiny reveals the superiority of our protocol in terms of security, as well as its efficiency in communication cost and computational overhead. These findings affirm the efficacy of our proposed solution in navigating the intricate interplay between medical robotics, blockchain, and data security.

Dummy filling is widely applied to significantly improve the planarity of topographic patterns for the chemical mechanical polishing (CMP) process in VLSI manufacturing. In the dummy filling flow, dummy synthesis works as the key step to adjust the post- CMP profile height. However, existing dummy synthesis optimization approaches usually fail to balance the filling quality and efficiency. This article proposes a novel model-based dummy filling synthesis framework NeurFill, integrated with multiple starting points-sequential quadratic programming (MSP-SQP) optimization solver. Inside this framework, a full-chip CMP simulator is first migrated to the neural network, achieving$8134\times $speedup on gradient calculation by backward propagation. Entrenched in the CMP neural network models, we further implement an improved version of NeurFill (pNeurFill) to alleviate the post- CMP height variation caused by dummy perimeter. After each iteration of dummy density optimization, an additional perimeter adjustment based on a given candidate dummy pattern set is applied to search for the optimal perimeter fill amount. The experimental results show that the proposed NeurFill outperforms existing rule- and model-based methods. The extra perimeter adjustment strategy in pNeurFill can achieve an average 66.97Å decreasing in height variation and 8.92% quality improvement compared to NeurFill. This will provide guidance for DFM so as to increase IC chip yield.