In double patterning lithography (DPL) layout decomposition for 45 nm and below process nodes, two features must be assigned opposite colors (corresponding to different exposures) if their spacing is less than the minimum coloring spacing [11, 9, 5]. However, there exist pattern configurations for which pattern features separated by less than the minimum color spacing cannot be assigned different colors. In such cases, DPL requires that a layout feature be split into two parts. We address this problem using a layout decomposition algorithm that includes graph construction, conflict cycle detection, and node splitting processes. We evaluate our technique on both real-world and artificially generated test cases in 45 nm technology. Experimental results show that our proposed layout decomposition method effectively decomposes given layouts to satisfy the key goals of minimized line-ends and maximized overlap margin. There are no design rule violations in the final decomposed layout.
Abstract Drug screening based on in-vitro primary tumor cell culture has demonstrated potential in personalized cancer diagnosis. However, the limited number of tumor cells, especially from patients with early stage cancer, has hindered the widespread application of this technique. Hence, we developed a digital microfluidic system for drug screening using primary tumor cells and established a working protocol for precision medicine. Smart control logic was developed to increase the throughput of the system and decrease its footprint to parallelly screen three drugs on a 4 × 4 cm 2 chip in a device measuring 23 × 16 × 3.5 cm 3 . We validated this method in an MDA-MB-231 breast cancer xenograft mouse model and liver cancer specimens from patients, demonstrating tumor suppression in mice/patients treated with drugs that were screened to be effective on individual primary tumor cells. Mice treated with drugs screened on-chip as ineffective exhibited similar results to those in the control groups. The effective drug identified through on-chip screening demonstrated consistency with the absence of mutations in their related genes determined via exome sequencing of individual tumors, further validating this protocol. Therefore, this technique and system may promote advances in precision medicine for cancer treatment and, eventually, for any disease.
Recent years have witnessed significant advances brought by microfluidic biochips in automating biochemical protocols. Accurate preparation of fluid samples is an essential component of these protocols, where concentration prediction and generation are critical. Equipped with the advantages of convenient fabrication and control, microfluidic mixers demonstrate huge potential in sample preparation. Although finite element analysis (FEA) is the most commonly used simulation method for accurate concentration prediction of a given microfluidic mixer, it is time-consuming with poor scalability for large biochip sizes. Recently, machine learning models have been adopted in concentration prediction, with great potential in enhancing the efficiency over traditional FEA methods. However, the state-of-the-art machine learning-based method can only predict the concentration of mixers with fixed input flow rates and fixed sizes. In this paper, we propose a new concentration prediction method based on graph neural networks (GNNs), which can predict output concentrations for microfluidic mixters with variable input flow rates. Moreover, a transfer learning method is proposed to transfer the trained model to mixers of different sizes with reduced training data. Experimental results show that, for microfluidic mixers with fixed input flow rates, the proposed method obtains an average reduction of 88% in terms of prediction errors compared with the state-of-the-art method. For microfluidic mixers with variable input flow rates, the proposed method reduces the prediction error by 85% on average. Besides, the proposed transfer learning method reduces the training data by 84% for extending the pre-trained model for microfluidic mixers of different sizes with acceptable prediction error.
Chemical-mechanical polishing (CMP) is one of the key steps during nanometer VLSI manufacturing process where minimum variation of layout pattern densities is desired. This paper proposes a novel optimal maze routing (OMR) algorithm which optimizes the layout uniformity as well as other routing objectives. The presented routing algorithm is optimal in the sense that it can find routing solutions for nets with minimum wire length, minimum number of vias and minimized layout uniformity-related cost. Experimental results show that compared with a previous routing algorithm, OMR can reduce the total number of vias by up to 24%. Except for the great improvement considering wire length and vias, the proposed routing algorithm also contributes a lot to minimizing the pattern density variation. Since current area fill methods are mostly based on fixed-dissection regime which cannot find the optimal filling solution for all possible floating windows, the proposed routing algorithm makes a good complement.
As EDA industry advances to smaller and smaller technology nodes, a tighter link between VLSI circuit manufacturing and physical design is becoming a necessity. This paper introduces several design for manufacturability (DFM) related problems such as critical area reduction, redundant via insertion, chemical-mechanical polishing (CMP), etc. Then the corresponding DFM-aware routing problems are formulated and solved using the proposed routing algorithms, respectively. Experimental results show that great yield enhancement can be obtained with a little runtime burden in routing, which proves the feasibility and effectiveness of considering DFM issues during the routing stage
With the rapid development of deep learning, training big neural network models demands huge amount of computing power.Therefore, many accelerators are designed to meet the performance requirements. Recently, series of Kunlun chips have been released, which claim comparable performance over GPUs. However, there lacks an end-to-end compiler to support both training and inference on Kunlun chip,leaving large performance optimization space to be explored. This paper presents KunlunTVM, the first end-to-end compiler based on TVM, supporting both training and inference tasks on Kunlun Chip. Experimental results show that KunlunTVM achieves up to 5x training performance improvement over the existing framework PaddlePaddle supporting Kunlun chip. It is noteworthy that the proposed methods are general and extensible for the TVM framework targeting different backends.
Digital microfluidic biochips (DMFBs) have become popular in the healthcare industry recently because of its lowcost, high-throughput, and portability. Users can execute the experiments on biochips with high resolution, and the biochips market therefore grows significantly. However, malicious attackers exploit Intellectual Property (IP) piracy and Trojan attacks to gain illegal profits. The conventional approaches present defense mechanisms that target either IP piracy or Trojan attacks. In practical, DMFBs may suffer from the threat of being attacked by these two attacks at the same time. This paper presents a comprehensive security system to protect DMFBs from IP piracy and Trojan attacks. We propose an authentication mechanism to protect IP and detect errors caused by Trojans with CCD cameras. By our security system, we could generate secret keys for authentication and determine whether the bioassay is under the IP piracy and Trojan attacks. Experimental results demonstrate the efficacy of our security system without overhead of the bioassay completion time.
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