Holographic phase enables quantitative observation of the morphology of bioprinted structures and holds strong potential in bio-fabrication. However, achieving accurate and rapid holographic phase reconstruction for real-time monitoring during 3D bioprinting remains a challenge. In this paper, we propose a novel Global Attention Phase Network (GAP-Net) that focuses on real-time and accurate phase reconstruction for holographic phase monitoring in bioprinting. The GAP-Net leverages lightweight feature extraction module and global attention for semantic segmentation of wrapped phases, enhancing reconstruction precision. In addition, by predicting the first six Zernike coefficients to capture the dominant background aberrations, the method effectively mitigates phase distortions and further improves reconstruction accuracy. The approach achieves real-time phase reconstruction at 22.8 FPS with a 1.11-fold accuracy improvement over existing deep learning methods. The proposed method also enables successful real-time visualization of the curing dynamics of biological samples during bioprinting, further demonstrating its practical effectiveness. This work not only provides real-time observational data for bioprinting but also shows promising potential for broader applications in fields such as optics and biomedical imaging.

Precise motion control of magnetic microrobots in complex and dynamic environments remains a critical challenge for enabling key applications such as targeted therapy and micromanipulation. Purely manual teleoperation is prone to operator fatigue and error, while fully autonomous systems often lack the robustness and adaptability to handle. Here, we propose a human-machine shared cascade control method for magnetically driven microrobots, which effectively integrates human cognitive intelligence with machine autonomy for collision-free navigation in dynamic environments. The outer-loop hybrid shared control unit smoothly modulates control authority in response to real-time collision risk, dynamically integrating the operator instructions and the autonomous navigation system output guided by the enhanced artificial potential field method to formulate the guidance law. For the inner-loop motion tracking, a data-driven adaptive orientation controller is designed, which integrates a nonlinear feedforward compensator leveraging a Gaussian process regression (GPR) model with a linear feedback controller whose parameters are optimized using the virtual reference feedback tuning (VRFT) method, ensuring fast and precise tracking of the desired motion. The effectiveness of the proposed method was validated through both simulation and physical experiments. In human-subject studies conducted on a physical magnetic actuation platform featuring both static and dynamic obstacle scenarios, quantitative results demonstrate that the shared control strategy significantly outperforms both purely manual and fully autonomous modes across all key metrics, including success rate, task completion time, stability, and safety ( $p \lt 0.001$ ). Furthermore, successful navigation within a complex gastric model demonstrates the potential of the shared control system for practical application in unstructured environments.

Magnetic soft millirobotic-grippers, equipped with agile pivot-walking motions and adaptive enveloping morph abilities, hold great promise for biomedical target acquisition tasks. However, deploying these millirobotic-grippers in highly constrained, disturbed, and variable terrains to realize multisequence target acquisition and transport tasks remains challenging. Here, we introduce a hierarchical multimodal motion control method for pivot-walking magnetic milliroboticgrippers, which enhances adaptive locomotion capabilities and enables high-precision motion control, facilitating autonomous target acquisition in complex terrains. The millirobotic-gripper utilizes a centrosymmetric three-pivot design, enabling adaptive soft enveloping deformation and robust multimodal locomotion. A hierarchical control architecture is proposed, comprising: 1) an upper-level Event-Based Finite State Machine planner that dynamically orchestrates transitions between motion modes according to environmental feedback and task-specific conditions; and 2) a lower-level Sliding Mode controller integrated with Gaussian Process-based gait parameter optimization, significantly improving motion accuracy and robustness against environmental disturbances. Experimental results demonstrate that our proposed method allows millirobotic-grippers to efficiently navigate morphing tunnels, leap across gaps exceeding 3 times their body length, accurately follow arbitrary paths with errors less than 5% of their body length, and reliably perform three types of targets grasping and transport. Furthermore, the biomedical application potential of our system is initially validated through ex vivo porcine gastrointestinal tract experiment with ultrasound guidance.

Magnetic helical microrobots have been widely applicated in environmental remediation, sensing, targeted medical applications, and so on. However, for locomotion and manipulation in unstructured liquid environments, the capabilities of distinguished motions and on-demand parking/starting over a team of microrobot are essential. Here, we propose a method for achieving on-demand motions conversion of helical microrobots by modulating surface wettability through surface chemical modification and surface microstructural modifications. An obvious difference shows that microrobot after chemical modification exhibit hydrophilicity and microrobot after microstructural modification exhibit hydrophobicity, where the latter possess higher moving step-out frequency and maximum forward velocity compared to the microrobot after surface chemical modification. The step-out frequencies and maximum velocities of the three types of microrobots (chemistry-modified, unmodified, and pimples-modified) are 13 Hz, 16 Hz, 22 Hz, and 385 μm/s, 511 μm/s, 649 μm/s. Furthermore, our method has demonstrated that can be employed to achieve effective on-demand targeted motions and modal conversion in liquid environment. We anticipate that the method can be potentially employed to achieve precise targeted drug delivery and surgery in biomedical applications.

Miniature robots hold great promise for performing micromanipulation tasks within hard-to-reach confined spaces. However, effectively maneuvering across complex and unstructured terrain, achieving adaptive morphogenesis, and developing adaptive multimodal locomotion strategies remain challenges for these robotic systems. Here, we develop a multi-stimulus-responsive deformable miniature robot integrated with an adaptive multimodal motion control method. Sodium alginate hydrogel and graphene-coated magnetic elastomer are integrated into the sheet-shaped robot to enable responsiveness to temperature, humidity, and magnetic fields. A kinematic gait model is designed to control oscillatory motion in the semi-contracted state and rotational motion in the fully contracted state of the miniature robot. To automatically mitigate angular deviation between the robot's motion direction and the intended path, an adaptive orientation compensation control algorithm based on Support Vector Regression (SVR) is proposed. Experimental results demonstrate that the proposed robot exhibits capabilities for flexible and accurate navigation within unstructured environments (e.g., rock piles and stomach models), and is further shown to be capable of cargo transport. The proposed adaptive morphogenesis robots, enabled by dual-mode motion control, hold significant potential for targeted delivery and other micromanipulation applications in complex, unstructured, and confined environments.

Magnetic helical microrobots have great potential in biomedical applications due to their ability to access confined and enclosed environments via remote manipulation by magnetic fields. However, achieving collision-free navigation for microrobots in complex and unstructured environments, particularly in highly dynamic settings, remains a challenge. In this paper, we present a novel deep reinforcement learning-based control framework for magnetic helical microrobots, focusing on the tasks of goal-reaching and dynamic obstacle avoidance. To streamline data collection, a specialized training environment capturing essential aspects of navigation for magnetic helical microrobots is devised. The robustness and adaptability of the trained policy are supported using a randomization technique within the training environment. To facilitate seamless integration with real-world magnetic actuation systems, a visual processing algorithm based on OpenCV is devised and incorporated to collect policy observations. Simulations and experiments in various scenarios validate the high robustness and adaptability of the method. The performance assessment revealed a success rate of 99% in navigating the microrobot around 4 dynamic obstacles of comparable speeds and a success rate of 90% in environments with 14 dynamic obstacles. The results indicate the potential for future applications of our method in unstructured, confined, and dynamic living environments.Note to Practitioners—The motivation of this work is to develop a robust and effective control scheme for collision-free navigation of magnetic helical microrobots in dynamic environments. The conventional navigation strategies in dynamic environments mainly include global path planning and local path replanning; thus, highly dynamic environments require frequent updates to the planned path, making it difficult to apply in highly dynamic environments. In this work, a deep reinforcement learning-based control framework is proposed that can guide microrobots through many dynamic obstacles to a series of locations without collisions. The simulation and experimental results validate the efficacy of the proposed control framework and the robustness and adaptability of the trained policy. The proposed control scheme enables better understanding of advanced motion control methods for magnetic microrobots.

Soft millirobots are highly promising for biomedical applications due to their reconfigurability and multifunctionality within physiological environments. However, the diverse and narrow biological cavity environments pose significant adaptability challenges for these millirobots. Here, we present a dual-morphology, thin-film millirobot equipped with a magnetic drive head and a functional tail to facilitate multimodal motion and targeted cell delivery. The millirobot can reversibly switch between two distinct morphologies in response to environmental stimuli through the deformation of its hydrogel body. Utilizing these dual morphologies, the millirobot can perform robust multimodal fundamental motions controlled by magnetic fields. We encapsulate fundamental motions with specific programmable magnetic field parameters into motion primitives, allowing easy invocation and adjustment of motion modes on demand. A knowledge graph is established to map terrain features to motion units, enabling the identification of optimal motion modes based on typical terrain characteristics. Experimental results indicate that the millirobot can effectively switch its morphology and movement modes to navigate various terrains, including narrow and curved channels as small as 1 mm, 0.8 mm high stairs with a 15° incline, and even the complex environment of a swine intestinal lumen. Its functional tail can carry immune cells to target and kill cancer cells. This robot can transport drugs and cells while navigating complex terrains through multimodal motion, paving the way for targeted medical tasks in intricate human environments in the future.

Microrobotic contact manipulation enables automated and precise cell capture, positioning and screening and has potential in biomedical engineering and disease detection. However, when using an optical microscope for visual positioning of targets, the poor clarity, limited cell-background contrast and lack of global 3D information of the cells in the field of view hinder global strategy making and automation, thereby affecting the accuracy and efficiency of manipulation. Here, we propose the 3D locating of multiple biological targets based on digital holography. Global–local combined visual feedback is developed for overall spatial locating and partial locating in a liquid-phase bright-field environment. By applying a filtering-based planar locating algorithm and maximum-area-based depth detection algorithm, the 3D global distribution of micro-targets in the field of view is periodically updated with a high detection rate. By applying a planar locating algorithm based on a convolutional neural network and a depth detection algorithm based on a gradient descent, the 3D fast locating of targets is performed precisely. Experiments show that the detection rate of the global positioning is 95.1%, the mean average precision of the local planar positioning is 90.53%, and the deviation of the local depth positioning is$1.22~\mu \text{m}$. When capturing cells, this method reaches an average speed of 7.4 cells/min and a collection rate of 90.5%. We anticipate that our method will support the research in cell-based bioengineering including cell screening and early disease diagnosis. Note to Practitioners—Automated cell manipulation is one of the most significant techniques in cell-based biomedical applications. This paper introduces a three-dimensional multi-target visual positioning method for automated cell manipulation. Combining with holographic imaging technique, the global information for the cells in the limited field of view is provided with improved imaging clarity and extended depth of field. The visual recognition algorithm can screen and extract the rare cells in the cell population, which will support the research in the field of early disease diagnosis and biomedicine. The research outcome provides an effective and precise solution to achieve biological targets positioning, capture, and screening within 3D liquid-phase bright-field environment.

Magnetic helical microrobots have attracted considerable attention in navigation control. However, the performance of microrobots is negatively affected by time-varying uncertain perturbations and obstacles, at the microscale. In this study, we present a navigation control scheme for accurately guiding the helical microrobot to targeted positions in dynamically changing environments. To efficiently plan smooth paths, a search-based algorithm with pruning rules is implemented to quickly find collision-free waypoints and design an optimal method with spatial and dynamic constraints for obtaining smooth paths globally. Velocity gain and potential fields are integrated to develop an emergency local motion replanning method for addressing random obstacles that suddenly appear in the preset path. In order to attain microrobot system dynamic linearization and achieve precise path following of a helical microrobot, a robust control strategy that integrates geometric and model-free controllers in a complementary manner is presented. The geometric controller as a feedforward controller, responsible for managing path information and generating guidance laws. In contrast, the model-free controller operates as a feedback controller, specifically designed to rapidly address position deviation. Meanwhile, we employ an observer to compensate for disturbances. Experimental results of precise motion control in both static and dynamic environments demonstrate the effectiveness of this navigation control scheme, which is promising for moving with high accuracy in cluttered and dynamic living enclosed environments.Note to Practitioners—This paper was motivated by the problem of the navigation control of magnetic microrobots in dynamic environment. The existing navigation control methods of microrobots mainly focus on the static environment, which is challenging to meet the emergency obstacle avoidance requirements in the cluttered environment with low Reynolds number. In addition, the conventional path following control always ignores the nonlinearity of the microrobot system, resulting in insufficient following accuracy. In this work, a novel navigation control method for microrobots is proposed, which can guide microrobots to accurately follow dynamically planned paths in cluttered environments without collision. Simulations and experiments validate the performance of the proposed navigation control method using helical microrobots. The proposed navigation control method paves the way for a better understanding of advanced navigation control method for magnetic microrobots.

Manipulating heterogeneous microtargets based on optoelectronic tweezers (OETs) to construct micropatterns with specific distribution and ordered arrangement enables recapitulating the spatial architecture of cells in native tissues, and has significant potential in tissue regeneration, medical diagnostics, and cell behavior research. However, the uncertain disturbances in liquid environment, collision risk, and electrokinetic interference in OETs system can cause microtargets to deviate from the safe and controlled state, especially for manipulation tasks with heterogeneous microtargets. Here, we propose an improved hierarchical value iteration network (IHVIN)-GAT-based path planning method for parallel manipulation of heterogeneous microtargets with independent control, integrating goal assignment, feature extraction, and decentralized decision-making. The Kuhn-Munkres-based goal assignment model periodically modifies the matching relationship between microtargets and goal positions to reduce the task complexity. High-order features involving path planning are extracted by an IHVIN model, and then selectively aggregated and convolved through GAT to yield real-time locomotion strategies for all microtargets. For the issues of constraint variability and system heterogeneity, discrete locomotion constraints are developed through analysis of escape mechanism, then embedded into modeling procedures and converted to heterogeneous edge weights in graph domain. The simulation and experimental results demonstrate the desired performance of the IHVIN-GAT model in high timeliness, high strategy quality, and compatibility for microtarget number, where up to 20 microtargets from three categories are parallel manipulated within 16 s to form arbitrary micropatterns recapitulating microscale architecture of cells in native tissues. We anticipate that our method will contribute to construct more biomimetic microstructures with heterogeneous cells for biomedical applications in the future.

Micron-range untethered, magnetic helical robots have great potential for biomedical applications due to their desirable performance with high flexibility and accuracy in unstructured and confined environments. However, at the microscale, time-varying uncertain disturbances in the environment and electromagnetic system greatly hinder helical microrobot tracking control performance. When a microrobot is replaced or even a derivative version with a slight helical body structure change is used for different tasks, the performance of the original control scheme remarkably decreases or even becomes ineffective. Here, we propose a data-driven optimal integrated controller (D2-OIC) that realizes precise tracking and transfer control among a series of helical microrobots with derived structures in different situations. The control approach has a parallel structure with nonlinear feedforward and linear feedback controllers. The nonlinear feedforward controller inversely maps the relationship between the electromagnetic field state and the helical microrobot motion state, allowing the helical microrobot to quickly approach the desired motion state. The linear feedback controller effectively adjusts the controller parameters using the virtual reference feedback tuning (VRFT) method, thus eliminating any residual motion errors arising from nonlinear control. By retraining on newly acquired and collected cumulative data with assigned weights, the nonlinear feedforward controller is updated to achieve transfer control among various helical microrobot types. In the experiment, two helical microrobot types performed arbitrary path tracking and obstacle avoidance tasks with tracking errors consistently less than 4% of the microrobot body length, demonstrating the feasibility of the proposed method.

Many previous works have facilitated muscle cell (C2C12) alignment to form fiber-like cell structures. However, there still remains a challenge how to induce C2C12 myoblasts in the cell structures to differentiate into matured myocytes to form a functional muscle tissue, while external mechanical stimulation has been proved to have good effects on proliferation and differentiation of myoblasts. In this paper, we proposed a vision-based micro robotic manipulation system to achieve automatic mechanical stimulation for one single muscle fiber-like cell structures (MFCS). A tube, which is attached to a three degree-of-freedom (DOF) manipulator, and a probe are employed to apply the uniaxial mechanical stimulation to train the MFCS. To measure the force applied on MFCS, a vision-based measuring and correction method is utilized, which decrease the error by 74%. Moreover, based on the viscoelastic property of the MFCS, a feedback control algorithm has been applied to compensate for the force loss to realize the force stimulation. And the final value of force remains 699 ± 1μN after 110s experiment.

The research on rare cells makes a significant contribution to biology research and medical treatment for the application of diagnostic operation as well as prognoses treatment. Therefore, sorting them from heterogeneous mixtures is crucial and valuable. Traditional cell sorting methods featured with poor purity and recovery rate as well as limited flexibility, which are not ideal approaches for rare type. In this paper, we proposed a cell screening method based on automated microrobotic aspiration-and-placement strategy under fluorescence microscope. An innovative autofocusing visual feedback (AVF) method is proposed for precise three-dimensional (3D) locating of target cells. For depth detection, multiple depth from defocus (MDFD) method is adopted to solve symmetry problem and attain an average accuracy of 97.07%. For planar locating, Markov random field (MRF) based locating method is utilized to separate and locate the overlapped cells. The end actuator locating and real-time tracking are performed relying on normalized cross-correlation (NCC) method. Experiential results show that our system collects rare cells (100 cells ml-1) at a speed of 5 cells min-1with 90% purity and 75% recovery rate, which is valuable for biological and medical application.

Microgrippers play a significant role in manipulation of micro-objects. To achieve dexterous and precise manipulation, a microgripper is required to be compactly designed and embedded with sensing feedback. Meanwhile, to convert the sensor position into displacement of the microgripper, the embedded sensors should be calibrated by additional equipment like laser sensor. However, a microgripper always needs to be calibrated during manipulation (online calibration), which is still a big challenge with current technology. In this paper, we proposed a highly compact microgripper integrated with position sensors, and a visual-based calibration method to handle such challenge. Moreover, to enhance grasping accuracy, flexure hinges are employed to achieve a low impedance grasping mechanism and to avoid the backlash in traditional bearing. Furthermore, kinematics analysis and Fine Element Analysis (FEA) are implemented to improve the design efficiency. Finally, fibrous micro-rings are successfully assembled, and the results reveal that the calibrated microgripper can be well employed to operate micro-objects.

Study of cellular behaviors, especially the ultra-rare cell type, can aid in the accuracy of clinic diagnoses as well as the development of bioresearch engineering, thus the importance of isolating them from heterogeneous mixtures. However, current methods may fail in purity, versatility or cause contamination to cell targets, which is fatal drawback to rare cells. To address this issue, we propose a versatile method to automatically select and capture fluorescent stained target cells with high purity and recovery rate, through developing a novel 3D image processing algorithm under dark field. With the automated pick-and-place strategies, the micro-robotic system achieves cell screening even in an environment with ultra-sparse cells. In the proposed visual method, Markov Random Field (MRF) separation is adapted into the fluorescent environment to attain real-time planar location of micropipette and target cells. A reformative method derived from Depth from Defocus (DFD) is brought up to acquire 3D information. The basic system for this method mainly consists of a camera mounted on motorized fluorescent microscope and a micromanipulator for cell capture. The fluorescent label help to screen out most of the undesired cells while also bring extra constraints and requisition to our visual method. Finally, experiments of collecting 3 T3 cells are performed to verify the feasibility and validity of the designed method, achieving average 98% purity and 80% recovery rate within the time limits. This study indicates that proposed visual processing method can not only provides reliable location feedback for micro-manipulation in rare cell sorting, but also can be easily extended to satisfy other automated micro-robotics manipulation.

In micromanipulation, lots of methods have been developed to manipulate objects in microscale. However, few of them can be applied in both the transportation and the rotation of the micro objects. In this paper, we present a novel method to realize the non-contact transportation and rotation of the micro objects based on the vibration-induced swirl flow. A piezo actuator is set between the glass needle and a metal rod. The sine wave with controlled frequency and amplitude is input into the piezo actuator to drive the glass needle to move circularly, which is caused by resonance of the actuator and the metal rod. We place the glass needle under water and keep a limited distance to the bottom. The circular vibration of the glass needle can generate a swirl flow and low pressure around it. The low pressure can trap and transport the micro objects vertically to the glass needle, and the swirl flow can rotate the objects continuously. Finally, we realize the trap and rotation of micro object with only one piezo actuator. Experiments of transportation and rotation of microbeads are carried out, and the results demonstrate it is a simple, low-cost, effective micromanipulation method.

Helical structure assembled by hydrogel microfibers is significant for culture of smooth muscle cells. However, the helical structure is only fabricated at the macroscale, while the fabrication of helical microstructure is still a challenge due to the lack of assembly method. In this paper, we propose a robotics-based assembly method to handle such challenge. An electromagnetic needle (EMN) is employed as end-effector to magnetically reel the microfiber encapsulating magnetic nanoparticles around a micropillar, and a dual-ring structure is designed to keep the microfiber being attracted at the EMN tip. For enhancing the stability of tip attraction, the manipulation mode of anticlockwise pushing microfiber is established. Moreover, the interaction mechanism between EMN tip and microfiber is analyzed by developing a static force model, and then the key condition of stably reeling microfiber is concluded. Furthermore, a robotics-based motion trajectory of EMN tip is planned to achieve a smooth reeling process. Based on such planning, the size of dual-ring structure is further optimized to improve the success rate of reeling. Finally, the helical microstructure with there-turn coils is successfully fabricated.

It is of great importance to pick up a single carbon nanotube (CNT) from a bulk of CNTs for nanodevice fabrication. In this study, we have proposed a nanorobotic manipulation system allowing automated pick-up of CNTs based on visual feedback. We utilize histogram normalization for automatic binarization, and it achieves to clearly distinguish CNTs from substrate and other impurities under different image brightness. Furthermore, we develop the gradient orientation inversion (GOI) algorithm to recognize CNT tip and atomic force microscopy (AFM) cantilever. Taking full advantages of the geometrical characteristics of CNT and AFM cantilever, GOI is proved to be quite robust. We have designed segment detection method (SDM) to successfully separate the AFM cantilever and CNT, whereas the contact detection between them is achieved by analyzing the straightness variation. Preliminary experimental results imply that our method shows high promise in realistic fabrication of nanodevices.

Due to the poor controllability in hydrogels, Hydrogels-based assembly to form larger 3D complex shapes is still a big challenge. In this paper, we have reported a novel “bottom-up” method to fabricate three-dimensional (3D) magnetic alginate microfibers (MAMs) assemblies with complex shapes. Specifically, Alginate microfibers encapsulating Fe3O4magnetic nanoparticles (MNs) and fibroblasts (NIH/3T3) have been spun using microfluidic method with “pinch-off” scheme. Experimental results show that the MAMs can respond quickly to the magnetic field, enabling their enhanced controllability. The magnetic assembly system is constructed by PDMS microfluidic device, 3D supporting model adhered on the bottom surface of dish filled with deionized water and magnet. The microfluidic “printing“ and magnetic deposition in magnetic assembly process are experimentally demonstrated, respectively. Because of magnetic field, the complex assembly shapes can be fabricated just by moving the microfluidic device in a plane. To match well with the shape of supporting model and to keep a stable assembly structure, the secondary cross-linking method is employed. From the LIVE/DEAD assay, cells can survive well during the magnetic assembly process.

Fabrication of artificial blood vessels in micro scale significantly benefits the regeneration of functional human vascular networks. In this paper, we develop an efficient multi-microrobotic system with an innovative motorized sample holder (MSH) and two manipulators. Air is injected into the solution through a glass pipette fixed on one manipulator to create bubbles. These bubbles conduct a regular rising movement, which is utilized to assemble the 2D ring-shaped microgels fabricated in a simple micro fluidic device. With this novel bubble-based method and the robotic system, we achieve the automation of the assembly. A 1.2 mm long vascular-like microtube with an outer diameter of 200 μm is fabricated. The whole process of the bubble-based assembly is visually observed and analyzed with side view. Key parameters are characterized to improve the assembly. Results show that the automated bubble-based assembly success rate is 100% and average time cost of assembling every microgel is as low as 3.25s.

3D assembly of cellular structures is important for the fabrication of biological substitute in tissue engineering. In this paper, a novel rail-guided multi-microrobotic system was proposed for the assembly of cellular structure. The cellular 2-dimensional (2D) module was fabricated by UV illumination of the crosslinkable hydrogel. The coordinated manipulation among the micromanipulators was performed with newly designed concentric movement along the rail, which realized the arbitrary change of micromanipulator posture. Through the rotation of the end-effectors around the specimen without swapping out the visual field, the manipulation flexibility was improved. The distance information between the micromanipulator and the module was acquired from vision feedback system and utilized for the automatic pick-up of the microstructure. Through the cooperation among multi-manipulators with hybrid motors, the micromanipulation to assemble the 3D structure with 30 nm operation resolution was achieved. Finally, the rail-guided DeSCom system realized the bottom-up fabrication of cellular vascular-like microtube with vision feedback.

Recently the research about constructing 3 dimensional cell structures is very important for its great potential applications in tissue engineering. In this paper, we report a novel method of constructing multi-layered microstructures embedding cells via microfluidic devices. The on-chip fabrication of movable microstructures embedding fibroblasts (NIH/3T3) based on Poly (ethylene glycol) Diacrylate (PEGDA) was reported. Two approaches for assembling these movable microstructures were presented. One was a manual assembly method based on micromanipulation system and the other one was a self-assembly method based on microfluidic channel. Several manual assembly ways were demonstrated and a tube-shaped microstructure with 17 layers was assembled by an efficient assembly method. A novel microfluidic channel was presented for conducting self-assembly method and a 2-layered experimental microfluidic device was fabricated by Polydimethylsiloxane (PDMS). The self-assembly process of fabricated microstructures via this device was preliminarily demonstrated.

Simultaneous multithreading (SMT) increases processor throughput by allowing parallel execution of several threads. However, fully sharing processor resources may cause resource monopolization by a single thread or other misallocations, resulting in overall performance degradation. Static resource partitioning techniques have been suggested, but are not as effective as dynamic ones since program behavior does change over the course of its execution. In this paper, we propose an Adaptive Resource Partitioning Algorithm (ARPA) that dynamically assigns resources to threads according to changes in thread behavior. ARPA analyzes the resource usage efficiency of each thread in a given time period and assigns more resources to threads which can use them more efficiently. Its purpose is to improve the efficiency of resource utilization, thereby improving overall instruction throughput. Our simulation results on a set of 42 multiprogramming workloads show that ARPA outperforms the traditional fetch policy ICOUNT by 55.8 percent with regard to overall instruction throughput and achieves a 33.8 percent improvement over Static Partitioning. It also outperforms the current best dynamic resource allocation technique, Hill-climbing, by 5.7 percent. Considering fairness accorded to each thread, ARPA attains 43.6, 18.5, and 9.2 percent improvements over ICOUNT, Static Partitioning, and Hill-climbing, respectively, using a common fairness metric. We also explore the energy efficiency of dynamically controlling the number of powered-on reorder buffer entries for ARPA. Compared with ARPA, our energy-aware resource partitioning algorithm achieves 10.6 percent energy savings, while the performance loss is negligible.

Simultaneous Multithreading (SMT) increases processor throughput by allowing the parallel execution of several threads. However, fully sharing processor resources may cause resource monopolization by a single thread or other misallocations, resulting in overall performance degradation. Static resource partitioning techniques have been suggested, but are not as effective as dynamically controlling the resource usage of each thread since program behavior does change during its execution.

Front-end instruction delivery accounts for a significant fraction of energy consumption in dynamically scheduled superscalar processors. Different front-end throttling techniques have been introduced to reduce the chip-wide energy consumption caused by redundant fetching. Hardware-based techniques, such as flow-based throttling, could reduce the energy consumption considerably, but with a high performance loss. On the other hand, compiler-based IPC-estimation-driven software fetch throttling (CFT) techniques result in relatively low performance degradation, which is desirable for high-performance processors. However, their energy savings are limited by the fact that they typically use a predefined fixed low IPC-threshold to control throttling. In this paper, we propose a compiler-based adaptive fetch throttling (CAFT) technique that allows changing the throttling threshold dynamically at runtime. Instead of using a fixed threshold, our technique uses the decode/issue difference (DID) to assist the fetch throttling decision based on the statically estimated IPC. Changing the threshold dynamically makes it possible to throttle at a higher estimated IPC, thus increasing the throttling opportunities and resulting in larger energy savings. We demonstrate that CAFT could increase the energy savings significantly compared to CFT, while preserving its benefit of low performance loss. Our simulation results show that the proposed technique doubles the energy-delay product (EDP) savings compared to the fixed threshold throttling and achieves a 6.1% average EDP saving.