The clinical application of 7-ethyl hydroxy-camptothecin (SN-38) maintains challenges not only due to its poor solubility and stability but also the lack of effective carriers to actively deliver SN-38 to deep tumor sites. Although SN-38-based nanomedicines could improve the solubility and stability from different aspects, the tumor targeting efficiency remains very low. Leveraging the hypoxic taxis of bifidobacteria bifidum (B. bifi) to the deep tumor area, we report SN-38-based nanomedicines-engineered bifidobacterial complexes for effective tumor-targeted delivery. Firstly, SN-38 was covalently coupled with poly-L-glutamic acid (L-PGA) and obtained soluble polymeric prodrug L-PGA-SN38 to improve its solubility and stability. To prolong the drug release, L-PGA-SN38 was mildly complexed with chitosan to form nanomedicines, and nanomedicines engineered B. bifi were further elaborated via electrostatic interaction of the excess of cationic chitosan shell from nanomedicines and anionic teichoic acid from B. bifi. The engineered B. bifi complexes inherited the bioactivity of native B. bifi and exhibited distinctly enhanced accumulation at the tumor site. More importantly, significantly elevated anti-tumor efficacy was achieved after the treatment of CS-L-PGA-SN38 NPs/B. bifi complexes, with favorable tumor suppression up to 80%. Such a B. bifi-mediated delivery system offers a promising platform for effective drug delivery and enhanced drug accumulation in the hypoxia deep tumor with superior anti-tumor efficacy

Escherichia coli NISSLE 1917 (EcN) is a Gram-negative strain with many prominent probiotic properties in the treatment of intestinal diseases such as diarrhea and inflammatory bowel disease (IBD), in particular ulcerative colitis. EcN not only exhibits antagonistic effects on a variety of intestinal pathogenic bacteria, but also regulates the secretion of immune factors in vivo and enhances the ability of host immunity. In this review, the mechanisms of EcN in the remission of inflammatory bowel disease are proposed and recent advances on the functionalized EcN are compiled to provide novel therapeutic strategies for the prevention and treatment of IBD.

ProbioticEscherichia coliNissle 1917 (EcN) with different modifications has exhibited multiple superiorities in the prevention and treatment of inflammatory bowel disease.

The clinical utility of 7-ethyl-10-hydroxycamptothecin (SN-38) is hampered by its low water solubility and reduced bioactivity at neutral or alkaline conditions. The rational design of an effective drug delivery system that can significantly enhance the therapeutic index of SN-38 and achieve complete tumor regression still remains a challenge. Herein, chitosan-based hybrid nanoparticles system co-loading with chemotherapeutic drug SN-38 and gold nanorods (AuNRs) was engineered for effective combinational photothermal-chemotherapy. To increase the solubility of SN-38, soluble polymeric prodrug poly (L-glutamic acid)-SN38 (L-PGA-SN38) was firstly synthesized and then complexed with chitosan to form stable nanomedicine via a mild and facile way without using any organic solvent or surfactant. Upon introducing AuNRs into chitosan-based nanomedicine by coordination interaction between the amine group of chitosan and AuNRs, the hybrid nanoparticles exhibited distinct synergistic therapeutic effect compared with single chemotherapy or photothermal treatment in vitro and in vivo. Almost complete tumor regression was achieved after 21-day treatment of the developed hybrid nanoparticles and showed no recurrence for at least 60 days.

7-Ethyl-10-hydroxycamptothecin (SN-38) as a potent anti-tumor candidate, suffers the constraints from its poor water solubility, pH-dependent lactone ring stability and the lack of efficient delivery system without losing its activity. Herein, biocompatible superparamagnetic chitosan-based nanocomplexes complexing with water-soluble polymeric prodrug poly(L-glutamic acid)-SN-38 (PGA-SN-38) was engineered for efficient delivery of SN-38. The manufacturing process of colloidal complexes was green, expeditious and facile, with one-shot addition of PGA-SN-38 into chitosan solution without using any organic solvent or surfactant. Upon introducing ultra-small-size superparamagnetic nanoparticles (~10 nm), the developed magnetic nanocomplexes exhibited significantly boosted tumor-targeted accumulation and efficient cellular internalization under a local magnetic field. Notably, the magnetic nanocomplexes achieved distinctly superior targeting and anti-tumor efficacy in the established xenograft colorectal cancer model of mice, with high tumor suppression rate up to 81%. Therefore, this superparamagnetic chitosan-based nanocomplex system could provide a promising platform for the targeted delivery of SN-38 in colorectal cancer therapy.

The development of smart size-tunable drug delivery nanoplatform enables the solving of the paradox of inconsistent size-dependence of high tumor accumulation and deep penetration during its delivery process, thus achieving superior cancer treatment efficacy. Herein, we report a size-shrinkable nanomicelle complex system with an initial size of 101 nm enabling effective retention around the tumor periphery and could destruct to ultrasmall nanomicelles triggered by a near-infrared (NIR) laser to realize the deep tumor penetration. The nanomicelle system is consisted of an upper critical solution temperature (UCST)-type block copolymer poly(acrylamide-acrylonitrile)-polyethylene glycol-lipoic acid (p(AAm-co-AN)-g-PEG-LA) encapsulating gold nanorods. Upon the irradiation of the NIR laser at the tumor site, gold nanorods could convert the light energy to heat energy, realizing the photothermal ablation of superficial tumor tissue. Concurrently, the large micelles split into a cascade of ultrasmall micelles (∼7 nm), which could easily penetrate into the deep site of the tumor and achieve the in situ "on-demand" release of the loaded drug to exert superior combined photothermal-chemotherapy of cancer. By the precise manipulation of laser, the micelle complex system realized the hierarchical killing from the superficial-to-deep tumor and achieved almost complete tumor growth inhibition on the established xenograft liver tumor mice model.

In the physical design of integrated circuits, global and detailed routing are critical stages involving the determination of the interconnected paths of each net on a circuit while satisfying the design constraints. Existing actual routers as well as routability predictors either have to resort to expensive approaches that lead to high computational times, or use heuristics that do not generalize well. Even though new, learning-based routing methods have been proposed to address this need, requirements on labelled data and difficulties in addressing complex design rule constraints have limited their adoption in advanced technology node physical design problems. In this work, we propose a new router: attention router, which is the first attempt to solve the track-assignment detailed routing problem using reinforcement learning. Complex design rule constraints are encoded into the routing algorithm and an attention-model-based REINFORCE algorithm is applied to solve the most critical step in routing: sequencing device pairs to be routed. The attention router and its baseline genetic router are applied to solve different commercial advanced technologies analog circuits problem sets. The attention router demonstrates generalization ability to unseen problems and is also able to achieve more than 100 times acceleration over the genetic router without significantly compromising the routing solution quality. We also discover a similarity between the attention router and the baseline genetic router in terms of positive correlations in cost and routing patterns, which demonstrate the attention router's ability to be utilized not only as a detailed router but also as a predictor for routability and congestion.

Efficient high-dimensional performance modeling of today's complex analog and mixed-signal (AMS) circuits with large-scale process variations is an important yet challenging task. In this paper, we propose a novel performance modeling algorithm that is referred to as Bayesian Model Fusion (BMF). Our key idea is to borrow the simulation data generated from an early stage (e. g., schematic level) to facilitate efficient high-dimensional performance modeling at a late stage (e. g., post layout) with low computational cost. Such a goal is achieved by statistically modeling the performance correlation between early and late stages through Bayesian inference. Several circuit examples designed in a commercial 32nm CMOS process demonstrate that BMF achieves up to 9x runtime speedup over the traditional modeling technique without surrendering any accuracy.

In this work, we propose a new technique to accurately decompose process variation into lot‐ to‐lot, wafer‐to‐wafer, wafer‐level spatially correlated, wafer‐level random, within‐die spatially correlated and within‐die random variation components. Performing such variation decomposition narrows down the main variation sources in the manufacturing process, and offers valuable information for process engineers to prioritize their goals in yield improvement to meet today's stringent time‐to‐market requirements. An important contribution of this work is to develop a physical dictionary which captures more spatially correlated systematic variation sources than the traditional quadratic modeling approach. We further propose the DCT (i.e., discrete cosine transform) dictionary to discover spatial patterns that are not modeled by the physical dictionary. Moreover, we propose to apply sparse regression to accurately extract the most adequate templates to represent spatially correlated variation for a particular process/design without over‐fitting. In addition, a robust sparse regression algorithm is proposed to automatically remove measurement outliers. Finally, we develop a fast numerical algorithm that may reduce the computational time of sparse regression by several orders of magnitude over the traditional implementation. The proposed variation decomposition technique has been validated by several synthetic data sets and silicon measurement data from advanced CMOS processes, which demonstrates that the proposed variation decomposition technique accurately models the spatially correlated systematic variation in the presence of outliers, and our improved numerical algorithm achieves significant speed‐up over the traditional implementation for large‐scale problems. For example, Fig. 1 shows a synthetic wafer map with 3 outliers. Fig. 2 shows the decomposed spatial variation containing quadratic, edge, center and random effects, each corresponding to different variation sources, where the outliers are automatically detected and removed.

In this paper, we propose a methodology based on unsupervised learning for automatic clustering of wafer spatial signatures to aid yield improvement. Our proposed methodology is based on three steps. First, we apply sparse regression to automatically capture wafer spatial signatures by a small number of features. Next, we apply an unsupervised hierarchical clustering algorithm to divide wafers into a few clusters where all wafers within the same cluster are similar. Finally, we develop a modified L-method to determine the appropriate number of clusters from the hierarchical clustering result. The accuracy of the proposed methodology is demonstrated by several industrial data sets of silicon measurements.

In this paper, we propose a new technique to achieve accurate decomposition of process variation by efficiently performing spatial pattern analysis. We demonstrate that the spatially correlated systematic variation can be accurately represented by the linear combination of a small number of templates. Based on this observation, an efficient sparse regression algorithm is developed to accurately extract the most adequate templates to represent spatially correlated variation. In addition, a robust sparse regression algorithm is proposed to automatically remove measurement outliers. We further develop a fast numerical algorithm that may reduce the computational time by several orders of magnitude over the traditional direct implementation. Our experimental results based on both synthetic and silicon data demonstrate that the proposed sparse regression technique can capture spatially correlated variation patterns with high accuracy and efficiency.

The discovery of patterns and correlations hidden in the test data could help reduce test time and cost. In this paper, we propose a methodology and supporting statistical regression tools that can exploit and utilize both spatial and inter-test-item correlations in the test data for test time and cost reduction. We first describe a statistical regression method, called group lasso, which can identify inter-test-item correlations from test data. After learning such correlations, some test items can be identified for removal from the test program without compromising test quality. An extended version of this method, weighted group lasso, allows taking into account the distinct test time/cost of each individual test item in the formulation as a weighted optimization problem. As a result, its solution would favor more costly test items for removal from the test program. We further integrate weighted group lasso with another statistical regression technique, virtual probe, which can learn spatial correlations of test data across a wafer. The integrated method could then utilize both spatial and inter-test-item correlations to maximize the number of test items whose values can be predicted without measurement. Experimental results of a high-volume industrial device show that utilizing both spatial and inter-test-item correlations can help reduce test time by up to 55%.

To enable statistical static timing analysis, for each cell in a digital library, a timing model that considers variations must be characterized. In this paper, we propose a dynamic method to accurately and efficiently characterize a cell's delay and output slew as a function of random mismatch variations. Based on a tight error bound for characterization using partial devices, our method sequentially performs simulations based on decreasing importance of devices and stops when the error requirement is met. Results on an industrial 32nm library demonstrate that the proposed method achieves significantly better accuracy-efficiency trade-off compared to other partial finite differencing approaches.

The aggressive scaling of IC technology results in large-scale performance variations that cannot be efficiently captured by traditional modeling techniques. This paper presents the recent development of statistical performance modeling and its important applications. In particular, we focus on two core techniques, sparse regression (SR) and Bayesian model fusion (BMF), that facilitate large-scale performance modeling with low computational cost. The basic ideas of SR and BMF are first explained and then their efficacy is compared to other traditional modeling approaches by using several analog and mixed-signal circuit examples.

Parametric yield estimation is one of the most critical-yet-challenging tasks for designing and verifying nanoscale analog and mixed-signal circuits. In this paper, we propose a novel Bayesian model fusion (BMF) technique for efficient parametric yield estimation. Our key idea is to borrow the simulation data from an early stage (e.g., schematic-level simulation) to efficiently estimate the performance distributions at a late stage (e.g., post-layout simulation). BMF statistically models the correlation between early-stage and late-stage performance distributions by Bayesian inference. In addition, a convex optimization is formulated to solve the unknown late-stage performance distributions both accurately and robustly. Several circuit examples designed in a commercial 32 nm CMOS process demonstrate that the proposed BMF technique achieves up to 3.75X runtime speedup over the traditional kernel estimation method.