During the IR Engineering Change Order (ECO) stage, cell moving leads to uncertain IR-drop results, requiring designers to explore multiple ECO candidates in each iteration to find a solution that effectively mitigates IR-drop, resulting in a long evaluation time. Although machine learning (ML)-based predictors have been proposed to expedite IR-drop evaluation, partial simulations are still needed to update features after ECO, taking over an hour and delaying IR-drop results. In this work, we propose a real-time dynamic IR-drop estimation method based on an XGBoost model with a global view of a cell’s surroundings. After ECO, our method provides dynamic IR-drop results in minutes without running any simulations and thus achieves real-time estimation. This allows designers to evaluate multiple ECO candidates concurrently in a single iteration. We conducted the experiments on five ECO candidates of an industrial design with 3 nm technology. The results show that the proposed model can effectively predict the IR-drop variations of moved cells after ECO with over $93 \%$ of fixed cells detected and an average MAE of 8.75 mV achieved. Furthermore, our method achieves an $88 X$ speedup over Voltus (commercial tool) and a $64 X$ speedup over traditional ML predictors when evaluating a single ECO candidate. The speedup is expected to increase as the number of ECO candidates increases.

Dynamic IR drop analysis is a critical step in the design signoff stage for verifying the power integrity of a chip. Since the analysis is extremely time-consuming, it has led to the emergence of machine learning (ML)-based methods to expedite the procedure. While previous ML approaches have demonstrated the feasibility of IR drop prediction, they often neglect package effects and do not address diverse IR criteria for memory and standard cells. Thus, this paper introduces a novel ML-based approach designed for a fast and accurate prediction of multi-type IR drop, considering package effects. We develop new package-related features to account for the package impact on IR drop. The proposed model is based on a multitask U-net architecture that not only predicts two types of IR drops simultaneously but also increases prediction accuracy through comprehensive learning. To further enhance the model performance, we introduce the Input Fusion Block (IFB), which unifies units across channels within the input feature maps, leading to improved prediction accuracy. The experimental results show the across-pattern transferability of the proposed IR drop prediction method, demonstrating an RMSE of less than SmV and an MAE of less than 2mV on the unseen simulation patterns. Additionally, our proposed method achieves a 5X speedup compared to the commercial tool.

With the advances in semiconductor technology, the sizes of transistors are getting smaller, which has led to an increasingly severe impact of IR drop. Consequently, this trend has amplified the significance of IR drop analysis within the realm of chip design. However, analyzing IR drop is resource-intensive and time-consuming, since numerous simulation patterns are required to verify the power integrity of circuits. Additionally, with every engineering change order (ECO) step, a reevaluation is necessary. In this paper, we propose a machine learning-based method to predict IR drop levels and present an algorithm for reducing simulation patterns, which could reduce the time and computing resources required for IR drop analysis within the ECO flow. Experimental results show that our approach can reduce the number of patterns by approximately 50%, thereby decreasing the analysis time while maintaining accuracy.

Simultaneously controlling the signs of each intersection in the arterial road sections grouping based on the concept of arterial signal progression is the trend of traffic control over the past few years since traffic conditions are usually regional and diffuse, and the regulation of a single intersection sign can only achieve limited improvement. While adjusting a signal controller using traditional method, there are multiple parameters needed to be confirmed and set. To further control the intersection of other continuing sections, it is required to switch the signs sequentially by entering different intersection operation interfaces, which not only has a higher error rate, but also makes it difficult to relieve the traffic flow at the first moment since there will be a time lag in the change of the signal at each intersection. In order to solve this problem, we propose a sensing-active event script operation mode and a UI design based on arterial traffic signal control on the basis of the existing traffic log network. When the system detects changes in the performance of road sections (heavy traffic or congestion), it will trigger the number control adjustment mechanism, which links the website controlled by the real-time signal sent through the network system to the communication group in the operator's mobile phone. This paper takes Chiayi County as the field demonstration. Through our proposed sensing-active event script operation mode and UI-based arterial traffic signal control, the work efficiency of the operator has increased, and the rate of input error in the number of seconds of the sign has reduced that thus, relieved the traffic flow at once.

A 400-800 MHz low noise amplifier (LNA) is designed and implemented for DTV-band cognitive radio applications. A feedforward noise canceling technique is applied to the LNA so that the sensitivity of the associated receiver and spectrum sensing system can be improved. Moreover, the design equation is derived for the noise canceling stage by considering the loading effect and parasitic capacitance. The LNA is fabricated using TSMC 0.18μm CMOS technology. Consuming the power of 16.81 mW from the 1.8-V supply, the LNA achieves the gain of 17.4 dB, NF of 1.35 dB and IIP3 of -5.11 dBm.

Automatic evaluation of photo aesthetic quality is a challenging problem in multimedia computing. Numerous aesthetic features have been proposed in previous works but the features are extracted solely from the photo under evaluation. In this paper, we explore the use of multiple images, and present the relative features that can be easily computed from any score-based features. We show that evaluation on a group basis can facilitate the quality assessment problem. Although the extraction of the new feature is extremely simple, computationally efficient, and requires no training phase, experimental results validate the effectiveness of the proposed approach.