Precise reagent mixing in user-defined ratios is a fundamental requirement in many microfluidic applications, including diagnostics, chemical synthesis, and biological assays. However, existing solutions for ratio-specific mixing often rely on complex active components, such as multiple pressure sources, flow controllers, or on-chip valves, making them costly, bulky, and unsuitable for portable or low-resource settings. In this work, we present a mixer design and a synthesis method for generating 3D-printable microfluidic devices that achieve ratiospecific mixing using only a single constant pressure source. Our method decomposes the desired mixing ratio into additive subcomponents, each represented by a dedicated inlet channel with a tailored length to enforce the correct hydraulic resistance. The method outputs a complete microfluidic layout, ready for direct fabrication via 3D printers. We validate our approach through numerical simulations and physical prototyping across eight diverse mixing scenarios. Results show that the achieved mixing ratios closely resemble the target, demonstrating the method’s accuracy and robustness. This work enables low-cost, portable, and accessible microfluidic devices for ratio-specific solution delivery, broadening the scope of microfluidics in settings where simplicity, reproducibility, and affordability are critical.

3D printing has revolutionized microfluidic device fabrication, enabling rapid prototyping and intricate geometries. However, designing lab-on-a-chip systems remains challenging. In microfluidic devices, precise control over fluid behavior is crucial, requiring careful attention to both timing and volume. Current state-of-the-art design automation tools for microfluidics have limitations, particularly in addressing the specific challenges of 3D-printed microfluidics and user-defined timing and volumetric constraints, and no design synthesis tool exists targeting these domains. We present 3M-DeSyn, a novel design synthesis method for 3D-printed microfluidics that incorporates timing and volumetric constraints and outputs print-ready 3D modeling files. It automates the design process, allowing users to specify schematics and desired flow control parameters. The underlying methodology is based on mathematical modeling of fluidic behavior and utilizes constraint optimization programming to find optimized solutions. Experimental results show significant improvements in design time while enabling rapid development of custom microfluidic systems.

As an emerging platform for biochemical experiments, flow-based microfluidic biochips are currently suffering from malfunctions caused by manufacturing defects, thereby having low yield. While many related studies have been conducted and reliability quantification models have been published, layout optimization methods are yet lacking. In this paper, we propose AutoRE, the first tool to automatically enhance reliability by optimizing layouts. AutoRE varies the layout within a certain range without changing its topology, and adopts Bayesian optimization (BO) to identify the most reliable variant. Experimental results demonstrate that AutoRE can efficiently and effectively improve the reliability across all testcases by around 40% on average.

Inkjet-printed electronics has attracted considerable attention for low-cost mass production. High-density inkjet-printed designs can benefit from printing and drying in batches to avoid defects due to undesired ink redistribution and ink merging. The state-of-the-art approach decomposes the design into small objects, assigns the objects to different layers to be printed in different iterations, and minimizes the number of layers to reduce the number of iterations. However, it overlooks the differences in the printing and drying time between different layers and thus cannot properly model the impact of different layer assignment solutions on the manufacturing cycle time. In this work, we propose a row-based printing model that simulates the inkjet-printing mechanism and an integral Gaussian drying model that evaluates the local evaporation rate to approximate the printing and drying process of inkjet-printed manufacturing. Based on these models, we propose a mixed-integer-linear programming (MILP) method called the manufacturing model to minimize the manufacturing cycle time and avoid defects by optimally assigning objects to different iterations to be printed and dried in batches. Experimental results confirm that, compared with the preliminary work, manufacturing using our optimized solutions required up to 42.7% less time.

Control channels on microfluidic large-scale integration (mLSI) chips are prone to blockage and leakage defects. In this work, we propose a built-in self-test (BIST) method that drastically improves the test efficiency. Given n to-be-tested control channels, we reduced the number of test patterns for blockage and leakage tests from [EQUATION] to 1, and from ⌈log2(n + 1)⌉ to ⌈log2(χ(G) + 1)⌉, respectively, where χ(G) denotes the vertex chromatic number of a graph G consisting of n vertices. We fabricated our design and demonstrated the feasibility and efficiency of our method.

After decades of development, flow-based microfluidic biochips have become an increasingly attractive platform for biochemical experiments. The fluid transportation and the on-chip device operation are controlled by microvalves, which are driven by external pneumatic controllers. To meet the increasingly complex experimental demands, the number of microvalves has significantly increased, making it necessary to adopt multiplexers (MUXes) for the actuation of microvalves. However, existing MUX designs have limited coding capacities, resulting in area overhead and excessive chip-to-world interface. This paper proposes a novel gate structure for modifying the current MUX architecture, along with a mixed coding strategy that achieves the maximum coding capacity within the modified MUX architecture. Additionally, an efficient synthesis tool for the mixed-coding-based MUXes (LaMUXes) is presented. Experimental results demonstrate that the LaMUX is exceptionally efficient, substantially reducing the usage of pneumatic controllers and microvalves compared to existing MUX designs.

In high-performance printed circuit boards (PCBs), adding serpentine delay lines is the most prevalent delay-matching technique to balance the delays of time-critical signals. Serpentine topology, however, can induce simultaneous accumulation of the crosstalk noise, resulting in erroneous logic gate triggering and speed-up effects. The state-of-the-art approach for crosstalk alleviation achieves waveform integrity by enlarging wire separation, resulting in an increased routing area. We introduce a method that adopts spiral delay lines for delay matching to mitigate the speed-up effect by spreading the crosstalk noise uniformly in time. Our method avoids possible routing congestion while achieving a high density of transmission lines. We implement our method by constructing a mixed-integer-linear programming (MILP) model for routing and a quadratic programming (QP) model for spiral synthesis. Experimental results demonstrate that our method requires, on average, 31% less routing area than the original design. In particular, compared to the state-of-the-art approach, our method can reduce the magnitude of the crosstalk noise by at least 69%.

Microfluidic large-scale integration (mLSI) is a promising lab-on-a-chip platform for high-throughput bio-applications. Due to the high integration scale and the small feature size, control channels on mLSI chips are prone to blockage and leakage defects, which may lead to faulty behavior of valves and erroneous experimental results. Thus, mLSI chips need to be tested before usage. Current mLSI-tests are mostly performed in a straightforward way by testing each valve individually, which is very time consuming and error prone. As the integration scale of mLSI chips keeps increasing, there is a pressing demand for more efficient test approaches. This work proposes the first built-in-self-test (BIST) method for mLSI with an integrated test module design. Instead of testing individual valves, the proposed method directly tests the control channels and thus greatly improves the test efficiency. Only${}({n}/{2})$and$\lceil \log _{2}(n+1)\rceil $test operations are required to test the blockage and leakage defects, respectively, of$n$control channels. The proposed test module consumes moderate area overhead and the test method is easy to operate. Neither specialized software nor external pressure sensors are required for carrying out the tests. Experiments show that our test approach is sensitive enough to detect defects that have a feature size as small as 10$\mu \text{m}$and that are several centimeters away from the test module.

Microfluidic large-scale integration (mLSI) biochips have developed rapidly in recent decades. The gap between design efficiency and application complexity has led to a growing interest in mLSI design automation. The state-of-the-art design automation tools for mLSI focus on the simultaneous co-optimisation of the flow and control layers but neglect potential contamination between different fluid reagents and products. Microfluidic switches, as fluid routers at the intersection of flow paths, are especially prone to contamination. State-of-the-art tools design the switches as spines with junctions, which aggregate the contamination problem. In this work, we present a contamination-free microfluidic switch design and a synthesis method to generate application-specific switches that can be employed by physical design tools for mLSI. We also propose a scheduling and binding method to transport the fluids with least time and fewest resources. To reduce the number of pressure inlets, we consider pressure sharing between valves within the switch. Experimental results demonstrate that our methods show advantages in avoiding contamination and improving transportation efficiency over conventional methods.

Design automation for continuous-flow microfluidic large-scale integration (mLSI) biochips has made remarkable progress over the past few years. Nowadays a biochip containing up to hundreds of components can be automatically synthesized within a few minutes. However, the current advanced design automation tools are mostly developed for research use, which focus essentially on the algorithmic performance but overlook the accessibility. Therefore, we have started the Cloud Columba project since 2017 to provide users from different backgrounds with easy access to the state-of-the-art design automation approaches. Without being limited by the computing power of their end devices, users just need to formulate their design requests in a high abstraction level, based on which the cloud server will automatically synthesize a customized manufacturing-ready biochip design, which can be viewed and stored using simply a web browser. With the computer-synthesized designs, Cloud Columba supports application developers to explore a wider range of possibilities, and algorithm developers to validate and improve their ideas based on a practical foundation.