Framework integrated collaborative design method of microfluidic FPVA biochip

Abstract

The invention belongs to the technical field of electronic automation of micro-fluidic biochips. The invention provides an architecture integrated collaborative design method of a microfluidic FPVA (Fibroblast Poly Vinyl Alcohol) biological chip. According to the embodiment of the invention, a high-order synthesis algorithm based on list scheduling is utilized, so that the logical association between the biochemical reagent and the microfluidic component can be obtained in a single scheduling process, and the design efficiency and quality of high-order synthesis are remarkably improved. A physical collaborative design algorithm based on ILP is utilized, component layout and flow channel wiring are considered as a whole, and a high-quality physical design scheme is generated with the goal of minimizing the total time for completing biochemical experiments and the total length of fluid transportation paths. The logic relationship between each operation and reagent in the sequence diagram structure is utilized to screen the operation or reagent needing to be constrained, then redundant constraints are deleted to reduce the size of the solution space of the ILP model, and the CPU running time of chip design is shortened.

Description

Technical Field

[0001] The disclosed embodiments relate to the field of electronic automation technology for microfluidic biochips, and in particular to a method for collaboratively designing an architecture of a microfluidic FPVA biochip. Background Art

[0002] Microfluidic chips are a kind of cutting-edge technology in the semiconductor field that is mainly based on electronics, computers and mechanics, and integrates multiple disciplines such as biology, chemistry, and medicine. It has been widely used in cell culture, protein crystallization, drug analysis and other fields. Through the precise control of nano / micro liquid reagents, various biochemical experiments can be automatically performed on highly integrated chips, and they have multiple advantages such as small size, high sensitivity, and fast speed. Therefore, they are also known as "chip laboratories". As a new generation of microfluidic system platform, the fully programmable valve array (FPVA) biochip has become one of the research hotspots in the current field. FPVA adopts a high-density array integrated architecture. The valves are arranged on the silicon substrate in the horizontal and vertical directions, and are connected to a peripheral multicast control system based on Boolean logic through control channels. Through the combination of valve switch states, flow paths in any direction and microfluidic components of different sizes can be dynamically constructed on the chip. Due to the use of an efficient array valve integrated architecture and advanced Boolean logic multicast control system, the FPVA chip not only maintains the various advantages of classic microfluidic chips, but also shows significant advantages in versatility, reconfigurability, and integration, and is expected to completely replace traditional application-oriented dedicated microfluidic devices.

[0003] As an important milestone in the development of microfluidics, the emergence of FPVA has significantly promoted the large-scale application of integrated circuit science in the fields of biochemical medicine, and on the other hand, it has also brought a series of new challenges to the field of electronic design automation (EDA) for biochips. Due to the adoption of a new integrated system, including large-scale array integration of valves, dynamically programmable modules, Boolean logic multicast control, etc., the complexity of FPVA chip architecture design has also increased dramatically. Balancing the model scale and target accuracy has become a difficult problem in biochip architecture design. As one of the most critical links in the chip EDA process, architecture synthesis converts high-level, abstract system descriptions (such as algorithms, data flow graphs, state machines, etc.) into synthesizable hardware structures, which can provide a good front-end design solution for subsequent logic synthesis and physical synthesis. Therefore, in view of the complex challenges of architecture synthesis under FPVA large-scale integration, accelerating the development of efficient and high-quality EDA tools has become a consensus in the industry and academia.

[0004] Currently, a variety of architectural synthesis methods have been proposed under the classic flow microfluidic biochip. However, although these methods can provide inspiration and reference for the architectural design of FPVA chips to a certain extent, EDA methods cannot be directly applied because FPVA chips use a new array-based integrated architecture with new features such as programmability, reconfigurability, and reusability. Therefore, it is necessary to propose a synthesis method under a new architecture, which is of great significance for the large-scale application and promotion of FPVA chips.

[0005] As a new product of the era of microfluidic technology development, FPVA chips have shown comprehensive advantages in versatility, reconfigurability, and integration while maintaining the advantages of classic flow chip architecture. In recent years, they have received great attention from industry and academia. Some preliminary explorations on FPVA chip architecture synthesis have been proposed. However, although existing methods have initially solved some key problems of FPVA architecture synthesis, they still have shortcomings in solution efficiency and solution quality. Summary of the invention

[0006] In order to avoid the shortcomings of the prior art, the present application provides an architecture-integrated collaborative design method for a microfluidic FPVA biochip to solve the problems of insufficient solution efficiency and solution quality in the prior art.

[0007] According to an embodiment of the present disclosure, a method for collaboratively designing an architecture of a microfluidic FPVA biochip is provided, the method comprising: A high-order synthesis algorithm based on list scheduling is used to obtain the logical association between biochemical reagents and microfluidic components in a single scheduling; With the goal of minimizing the total time to complete the biochemical experiment and the total length of the fluid transport path, an initial layout and routing scheme was designed using an ILP-based collaborative physical design algorithm; Based on the logical association between biochemical reagents and microfluidic components, the initial layout and routing scheme is optimized using the pruning technology based on the timing graph structure to obtain the target layout and routing scheme.

[0008] Further, the step of obtaining the logical association between the biochemical reagents and the microfluidic components in a single scheduling by using a high-order comprehensive algorithm based on list scheduling includes: Read the data description of the timing diagram and convert it to obtain the basic structure diagram of the microfluidic component; Using the basic structure diagram, calculate the minimum delay schedule, and assign a start time and an end time to each schedulable component based on the minimum delay schedule; The components in the basic structure graph are scheduled using a high-order synthesis algorithm based on list scheduling. In the list scheduling process, all operations with an in-degree of 0 are first queued, and then iterated in the queue to schedule each dequeued operation and its successor operation, update its start time and end time, and queue the successor operation; Until the queue is empty, all operations have been scheduled. In the above process, the relationship between biochemical reagents and operations is recorded to generate a logical relationship array between biochemical reagents and microfluidic components.

[0009] Furthermore, the list-based high-level synthesis algorithm includes: Input mixed operands n , the operation set is ops [ s ], No. s The mixing time for each operation is Optime(o s ),operate s The start time is start_time s The operation ends at end_time s , the number of edges in the timing graph m , the edge set of the timing graph E ={ a , b};in, a and b Represents a sequence of operations: a arrive b ; The in-degree and out-degree of each operation are inDegree ( o s )and outDegree ( o s ), array v [ j ][ s ] indicates operation s Is it a reagent? j destination, array out [ j ][ s ] indicates reagent j Whether it is operated s After completion, v [ j ][ s ]and out [ j ][ s ]The default values ​​are all 0; Iterate through each operation s Right now o sTo check whether its degree is 0; if o s If the degree is 0, put it in the queue Q In the start_time x Set to 0; then, the association enters o x Reagents j With operation s Concurrently v [ j ][ s ] is 1, indicating o s For reagents j The target component; Iterating over the queue Q , retrieve the queue head o s , and set the end time of the operation end_time s , the formula is as follows: (1) in, Optime ( o s )express o s Operation time; get o s Subsequent operations o k , by setting v [ kin ][ k ] is 1 to associate the inflow operation k Reagents kin ; By out [ kout ][ k ] Set to 1 to associate the slave operation k Output reagents kout ; like o k is ready to be scheduled, then o k Join the queue Q ;in, o k The start time is updated according to the following formula: (2) in, p 1 for o k The first ancestor of p 2 for ok The second ancestor of p 3 for o k The third ancestor, r Time for fluid delivery.

[0010] Furthermore, with the goal of minimizing the total time to complete the biochemical experiment and the total length of the fluid transport path, the ILP-based collaborative physical design algorithm includes: objective functions, component constraints, and fluid constraints; Among them, component constraints include component direction coordinate uniqueness constraint, component coordinate direction constraint, component continuity constraint, component position relationship constraint, component conflict constraint, component end time constraint and component start time constraint; Fluid constraints include fluid starting point constraints, fluid end point constraints, fluid starting movement constraints, fluid end point movement constraints, fluid movement constraints, starting time constraints, fluid time constraints, reagent time relationship constraints, fluid starting point adjacent time constraints, fluid end point adjacent time constraints, fluid adjacent time constraints, fluid and coordinate axis constraints, inter-fluid constraints and component fluid constraints.

[0011] Furthermore, the expression of the objective function is: (3) in, The first parameter is a value between 0 and 1. The second parameter is a value between 0 and 1. and Add up to 1; (4) (5) in, For reagents j The end time of O is a collection of components, N is a fluid collection, For reagents j Is it located in ( x , y ), W is the chip width, H is the chip height.

[0012] Furthermore, the component direction coordinate unique constraint: any direction of the component appears only once in the coordinate axis, and its constraint formula is: (6) in, For component collection O Components in i Is each direction occupied?x , y ), if type is 1, it means component collection O The lower left corner occupies the type 2, indicating a component collection O The lower right corner is occupied by type 3, indicating a component collection O The upper left corner occupies the space of the component. Type 4 indicates a component collection. O The upper right corner is occupied; Component coordinate direction constraint: Component type appears at any coordinate, and its constraint formula is: (7) Component continuity constraint: The position occupied by the component is a continuous piece, and its constraint formula is: (8) Component position relationship constraints: Components cannot occupy input and output ports. The constraint formula is: (9) Conflict constraints between components: The position relationship of components constrains the reuse of the same area between components at different times. The constraint formula is: (10) in, Arrival component for the first reagent i The time of the component i The placement time, For components k End time of Component end time constraint: The end time occupied by a component is the latest start time plus the operation time. The constraint formula is: (11) in, Components i End time of Component start time constraint: The earliest start time of a component is the arrival time of the first reagent, and the latest start time is the arrival time of the last reagent. The constraint formula is: (12) in, Arrival component for the last reagent i time.

[0013] Furthermore, the fluid starting point constraint: each reagent starts from its starting point, and the starting time of the reagent is when it is at the starting point. The constraint formula is: (13) in, Reagents j First arrival (x , y ) time, For reagents j The start time, For reagents j Whether it is the final output fluid, For reagents j Is it a mixed component? i produce; Fluid endpoint constraint: Each reagent reaches a target position, and the end point is the end time of the reagent. The constraint formula is: (14) in, For reagents j The end time of For components i Is it a reagent? j The target component; Fluid initial movement constraint: If the reagent is at the starting position, then one of the adjacent coordinates of the starting position must be occupied. The constraint formula is: (15) Fluid endpoint movement constraint: If the reagent is at the target position, then one of the adjacent coordinates of the starting position must be occupied. The constraint formula is: (16) Fluid movement constraints: If the current reagent occupies ( x , y ) and is not at the starting position or has not reached the target position, the reagent will flow to the adjacent coordinates, and the constraint formula is: (17) Start time constraint: The start time of the input reagent is multiplied by the length of the reagent. The constraint formula is: (18) in, is the total length of the reagent; Fluid time constraint: The difference between the end time and the start time of the reagent is equal to the total length of the grid that the fluid passes through plus the length of the reagent itself. The constraint formula is: (19) Reagent time relationship constraint: the starting time of the reagent is equal to the corresponding component i The end time of , its constraint formula is: (20) Fluid start point adjacent time constraints: (twenty one) Fluid endpoint adjacent time constraints: (twenty two) Fluid Adjacent Time Constraints: (twenty three) Fluid and coordinate axis constraints: When a coordinate is occupied by a reagent, the time of the coordinate is enabled and recorded, and the constraint formula is: (twenty four) (25) Inter-fluid constraint: During fluid transport, two reagents occupy the same grid cell in non-overlapping time. The constraint formula is: (26) Component fluid constraint: In the fluid transport path, if a non-target component is encountered, the reagent passes through before the component occupies the unit or after the component is executed. The constraint formula is: (27) (28) (29).

[0014] Furthermore, the pruning technology based on the timing graph structure includes: Pruning technology based on component relationship, pruning technology based on fluid relationship, pruning technology based on component fluid relationship and pruning technology based on fluid endpoint; among them, Using the pruning technology based on component relationships, the conflict constraints between components are pruned to obtain: (30) in, For components i and components k Whether they are grandparents or grandchildren, if not, restrict their overlapping relationship; Using the pruning technology based on fluid relations, the constraints between fluids are pruned to obtain: (31) in, Reagent j and reagents e Whether they are grandparents and grandchildren, if not, restrict their conflicting relationship; Using the pruning technology based on component fluid relations, the component fluid constraints are pruned to obtain: (32) (33) in, Representation Components i Is it a reagent? j ancestor component, otherwise constrain its conflict relationship; Using the pruning technology based on fluid endpoint, the fluid endpoint constraint and reagent time relationship constraint are pruned to obtain: (34) (35).

[0015] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects: In the embodiments of the present disclosure, through the above-mentioned microfluidic FPVA biochip architecture integrated collaborative design method, on the one hand, a high-order synthesis algorithm based on list scheduling is proposed, which can obtain the logical association between biochemical reagents and microfluidic components in a single scheduling process, significantly improving the design efficiency and quality of high-order synthesis. A physical collaborative design algorithm based on ILP is proposed, which takes into account the component layout and flow channel wiring, and generates a high-quality physical design scheme with the goal of minimizing the total time to complete the biochemical experiment and the total length of the fluid transport path. The logical relationship between each operation and the reagent in the timing diagram structure is used to screen the operations or reagents that need to be constrained, and then the redundant constraints are deleted to reduce the solution space size of the ILP model. On the other hand, the layout and wiring issues are taken into account, and the overall performance of the chip is significantly improved through integrated design and optimization, the total execution time of the biochemical experiment is reduced to the greatest extent, the total length of the fluid transport path is optimized, and the CPU running time of the chip design is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0017] Figure 1 A diagram showing the steps of a method for co-designing an architecture of a microfluidic FPVA biochip in an exemplary embodiment of the present disclosure; Figure 2 The time relationship between fluid movement and component operation in the FPVA biochip in the exemplary embodiment of the present disclosure is shown; Figure 3 A schematic diagram of a scene showing a fluid start movement constraint, a fluid end movement constraint, and a fluid movement constraint in an exemplary embodiment of the present disclosure; Figure 4A schematic diagram showing the time relationship of fluid in an exemplary embodiment of the present disclosure is shown; Figure 5 A schematic diagram illustrating components / fluid constraints in an exemplary embodiment of the present disclosure is shown; Figure 6 A timing diagram including 4 inputs, 3 mixes and 1 output in an exemplary embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0018] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0019] In addition, the accompanying drawings are only schematic illustrations of the embodiments of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated descriptions will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities.

[0020] This example embodiment provides a method for co-designing an architecture of a microfluidic FPVA biochip. Figure 1 As shown in , the architecture-integrated collaborative design method of the microfluidic FPVA biochip may include: steps S101 to S103.

[0021] Step S101: using a high-order comprehensive algorithm based on list scheduling to obtain the logical association between biochemical reagents and microfluidic components in a single scheduling; Step S102: Designing an initial layout and routing scheme using an ILP-based collaborative physical design algorithm with the goal of minimizing the total time required to complete the biochemical experiment and the total length of the fluid transport path; Step S103: Based on the logical association between the biochemical reagents and the microfluidic components, the initial layout and wiring scheme is optimized using a pruning technique based on a timing graph structure to obtain a target layout and wiring scheme.

[0022] Through the above-mentioned architecture-integrated collaborative design method of microfluidic FPVA biochip, on the one hand, a high-order synthesis algorithm based on list scheduling is proposed, which can obtain the logical association between biochemical reagents and microfluidic components in a single scheduling process, significantly improving the design efficiency and quality of high-order synthesis. A physical collaborative design algorithm based on ILP is proposed, which takes into account the component layout and flow channel wiring, and generates high-quality physical design solutions with the goal of minimizing the total time to complete the biochemical experiment and the total length of the fluid transport path. The logical relationship between each operation and reagent in the timing diagram structure is used to screen the operations or reagents that need to be constrained, and then the redundant constraints are deleted to reduce the solution space size of the ILP model. On the other hand, the layout and wiring issues are taken into account, and the overall performance of the chip is significantly improved through integrated design and optimization, which reduces the total execution time of the biochemical experiment to the greatest extent, optimizes the total length of the fluid transport path, and reduces the CPU running time of the chip design.

[0023] Next, we will refer to Figures 1 to 6 The various steps of the above-mentioned microfluidic FPVA biochip architecture integrated collaborative design method in this example embodiment are described in more detail.

[0024] In step S101, high-level synthesis is implemented by using a list-based scheduling method. The list-based high-level synthesis algorithm mainly includes the following steps: Step 1: Read the data description of the timing diagram and convert it to obtain the basic structure diagram of the microfluidic component.

[0025] Step 2: Using the structure graph, calculate the minimum delay schedule and assign start and end times to each schedulable component.

[0026] Step 3: Use the list scheduling algorithm to schedule the components in the basic structure diagram. In the list scheduling process, first, all operations with an in-degree of 0 are queued, and then the queue is iterated to schedule each dequeued operation and its subsequent operations, update their start time and end time, and queue the subsequent operations. Until the queue is empty, all operations have been scheduled. In the above process, the relationship between reagents and operations is recorded to generate a relationship array.

[0027] Using a high-order comprehensive algorithm based on list scheduling to obtain the logical association between biochemical reagents and microfluidic components in a single scheduling can greatly improve the overall efficiency and resource utilization. The high-order comprehensive algorithm based on list scheduling (i.e., Algorithm 1) is specifically as follows:

[0028] The specific definition and process of the list-scheduling-based high-order synthesis algorithm are as follows.

[0029] Assume that a timing diagram of a fluid operation is given, where n operations, and the operation set is ops [ s ], No. s The mixing time for each operation is Optime(o s ),operate s The start time is start_time s The operation ends at end_time s , the in-degree and out-degree of each operation are inDegree ( o s )and outDegree ( o s ), array v [ j ][ s ] indicates operation s Is it a reagent? j destination, array out [ j ][ s ] indicates reagent j Whether it is operated s After completion, the default values ​​of both arrays are 0. The algorithm flow is as follows.

[0030] The algorithm iterates through each operation s to check if its degree is 0. If o s If the degree is 0, put it in the queue Q In the start_time s Set to 0. Then, the association enters o s Reagents j With operation s Concurrently v [ j ][ s ] is 1, indicating o s For reagents j The target component (i.e., lines 2-10 in the algorithm).

[0031] After completing the above operations, the algorithm traverses the queue Q , retrieve the queue head o s , and set the end time of the operation end_time s , the formula is as follows.

[0032] (1) in, Optime ( o s )express o s operation time.

[0033] Then, get o s Subsequent operations o k , by setting v [ kin ][ k ] is 1 to associate the inflow operation k Reagents r kin Similarly, by out [ kout ][ k ] Set to 1 to associate the slave operation k Output reagents r kout (Lines 14-22 in Algorithm 1).

[0034] Finally, if o k is ready to be scheduled, then o k Join the queue Q . o k The start time is updated according to the following formula.

[0035] (2) in, p 1 for operation k The first ancestor of p 2 for operation k The second ancestor of p 3 for operation k The third ancestor of the genus, and so on. r Time for fluid delivery.

[0036] In step S102, in order to achieve collaborative design between layout and routing to minimize the total time to complete the biochemical experiment and the total length of the fluid transport path, the present application designs a physical design algorithm based on ILP. The commonly used symbol settings of the algorithm are shown in Table 1, where out ij , v ij , last j are the three relationship numbers obtained in the high-order synthesis algorithm. W , H , O , NThe symbols represent chip width, chip height, component set, and fluid set respectively. The objective function and related constraint definitions are shown in Table 1.

[0037] Table 1 Symbols commonly used in wiring and layout constraints

[0038] Objective function: Minimize the total time of biochemical experiments z and fluid transport pathways r , expressed as a multi-objective weighted sum, the objective function is as follows: (3) right z and r Constraints: (4) (5) Where, formula (4) represents z is the maximum time taken by all types of reagents to reach the destination, that is, the end time of the output reagent. Formula (5) represents the sum of the last unit flow path of all reagents.

[0039] Component Constraint Design: Component direction coordinate unique constraint: Any direction of a component appears only once in the coordinate axis. The constraint formula is as follows: (6) This formula ensures that the component appears uniquely in each direction and will not appear in other coordinates.

[0040] Component coordinate direction constraint: The maximum number of times a component type (upper left, lower left, upper right, lower right) appears at any coordinate is 1, that is, a component in one direction can only appear once. The constraint formula is as follows: (7) This formula ensures that the directions of multiple components cannot appear repeatedly within any coordinates, and together with formula (4), it ensures the uniqueness of the component appearance.

[0041] Component continuity constraint: The position occupied by the component should be a continuous piece. The constraint formula is as follows: (8) This formula ensures the continuity of component coordinate placement on the chip. i If the lower left direction is occupied, then the other directions should also be occupied.

[0042] Component position relationship constraints: Components cannot occupy input and output ports. The constraint formula is as follows: (9) In the chip, the output port is located at the lower left point and the input port is located at the upper right point. This formula is very important to prevent the reagent from blocking the input and output ports during the inflow and outflow process and causing conflicts.

[0043] Component conflict constraints: Component position relationships constrain components to reuse the same area at different times. The constraint formula is as follows: (10) This formula combined with formula (6) and (7) only allows two components to overlap when their time does not overlap, thus ensuring the uniqueness of the components.

[0044] Component end time constraint: The end time occupied by a component should be the latest start time plus the operation time. The constraint formula is as follows: (11) Component start time constraint: The earliest start time of a component is the arrival time of the first reagent, and the latest start time is the arrival time of the last reagent. The constraint formula is as follows: (12) This formula means that if j For components i The target component is i The earliest start time should be smaller than the end time of all reagents arriving at the component. The latest start time should be larger than the end time of all reagents arriving at the component.

[0045] like Figure 2 As shown in the figure, the three reagents start from the input port in the upper right corner of the grid and reach their respective target components without conflict. The placement time of the component is displayed in the upper right corner, and the latest end time of the component is displayed in the lower left corner. The intermediate reagents generated after the component ends will continue to reach their target components. The final output reagent is released from the output port in the lower left corner of the grid. From formula (12), it can be seen that with the operation o The component bound to 5 starts to be placed when the reagent 6 arrives at time 19, and the operation starts at the latest when the reagent 7 arrives at time 28. Formula (11) constrains, operation o 5 must end no later than its latest start time 28 plus the operation duration 10.

[0046] Fluid Constraint Design Fluid starting point constraint: Each reagent can only start from its starting point, and the starting time of the reagent is when it is at the starting point. The constraint formula is as follows: (13) This formula means that if the reagent j is a non-input reagent (including intermediate reagents and output reagents) and the reagentj From component i The output of the reagent j It will start from the lower left of the component; if it is an input reagent, it will start from the input port. The time when it reaches the coordinate of this point is the starting time of the reagent.

[0047] Fluid endpoint constraint: Each reagent can only reach one target position, and the end point is the end time of the reagent. The constraint formula is as follows: (14) This formula means that if the reagent j If it is an output reagent, it will reach the output port at a certain time. If it is an input reagent or an intermediate reagent, it will reach the destination component at a certain time. And the time at the output port is the final time. Formula (11) and formula (12) together ensure that the fluid starts from the starting point and eventually reaches the end point.

[0048] Fluid initial movement constraint: If the reagent is at the starting position, then one of the adjacent coordinates of the starting position must be occupied. The constraint formula is as follows: (15) This formula ensures that the reagent can start from the starting position and move in only one direction, that is, there must be an adjacent coordinate occupied by the reagent at the starting position.

[0049] Fluid endpoint movement constraint: If the reagent is at the target position, then one of the adjacent coordinates of the starting position must be occupied. The constraint formula is as follows: (16) This formula ensures that the reagent will not continue to move in other directions after reaching the destination, and only the coordinates in the direction of reaching the output port are occupied, that is, the reagent has reached the destination.

[0050] Fluid movement constraints: If the current reagent occupies ( x , y ) and is not at the starting position or has not reached the target position, the reagent will flow to the adjacent coordinates. The constraint formula is as follows: (17) This formula represents the movement rule of the component: the reagent can only move to the adjacent coordinates from the current position at the next moment under the premise that movement is allowed at the current moment, and the premise of movement is to ensure that the current position does not encounter the target component, otherwise it can be regarded as having reached the destination. Formula (13), formula (14), and formula (15) ensure the mobility of the reagent during the transportation process, that is, continuous movement from the destination to the destination.

[0051] Figure 3 (a) shows thatt = 1 moment, by o 1 The generated blue reagent is working towards its target o 4 The corresponding components move. Enter the next time unit t =2, assuming the length of the reagent is 2 grid units, Figure 3 (b) and (c) depict two possible directions of movement of the reagent. The above three formulas ensure the continuous trajectory of the reagent. By optimizing the objective function, Figure 3 The reagent path in (d) strives to minimize its length, Figure 3 The trajectory in (c) is adjusted to the optimal direction.

[0052] Start time constraint: The value of the start time of the input reagent is (0 minus the number of input reagents) multiplied by the length of the reagent. The constraint formula is as follows: (18) This formula avoids multiple reagents being input at the input port at the same time, and assigns an input start time to the input reagents, with the time interval being the length of the reagent.

[0053] Fluid time constraint: The difference between the end time and the start time of the reagent is equal to the total length of the grid that the fluid passes through plus the length of the reagent itself. The constraint formula is as follows: (19) This formula indicates j The end time of a reagent should be equal to its start time plus the length of the flow path plus the length of the reagent itself. Figure 2 In , it is assumed that each reagent is 2 grid units long and the start time of the intermediate reagents coincides with the end time of their respective source components. The duration of each reagent to reach its target component is calculated as the sum of its start time, the path length it has traveled, and the length of the fluid itself. For example, in t =12, from operation o 1 of reagents 4 in reaching the target operation o 4( t =16), it has to go through 2 path lengths plus its own length.

[0054] Reagent time relationship constraint: the start time of the reagent is equal to the corresponding source component i End time , the constraint formula is as follows: (20) If the component i For reagents j The source components, then the reagents j The start time should be equal to the component i completion time.

[0055] Fluid start point adjacent time constraints: (twenty one) This formula means that at the starting position of the reagent, while occupying the next coordinate, the time will be at the starting position time Add 1 to the basis.

[0056] Fluid endpoint adjacent time constraints: (twenty two) This formula indicates that at the end point of the reagent, the time of its adjacent coordinates can only be related to the time corresponding to the previous position of the destination coordinate. Subtract 1 from the basis.

[0057] Fluid Adjacent Time Constraints: (twenty three) like Figure 4 As shown, the yellow grid indicates that it has been occupied by the reagent, and the light green grid indicates the adjacent grid at (x, y). The sum of the time of adjacent coordinates can be expressed as .

[0058] Fluid and coordinate axis constraints: The time of the coordinate can only be enabled and recorded when the coordinate is occupied by a reagent. The constraint formula is as follows: (twenty four) (25) Wherein, formula (24) means that if j If the reagent occupies a coordinate, the time corresponding to the coordinate should be greater than 0. Formula (25) indicates that the time should be equal to 0 at a coordinate without a flow path. Formula (24) and formula (25) are formulas (21)-(23) excluding irrelevant time, so that the equation is established.

[0059] Inter-fluid constraint: During fluid transport, two reagents can only occupy the same grid cell during non-overlapping time. The constraint formula is as follows: (26) This formula means that if two reagents occupy the coordinates ( x , y ), then the time when the two reagents arrive at the coordinate cannot overlap. Figure 5 As shown in (a), at time point t = 1, the grid where the red reagent moves forward is occupied by the blue reagent, and the movement may cause the flow channel to cross. Therefore, it is necessary to limit the reagents from occupying the same grid unit at different times. Figure 5 As shown in (b), the blue reagent t=0 to t =1 occupies the channel, while the red reagent t =2 to t =3 occupies the channel, effectively preventing conflicts.

[0060] Component fluid constraint: Fluid component constraint. In the fluid transportation path, if a non-target component is encountered, the reagent can pass before the component occupies the unit or after the component is executed. The constraint formula is as follows: (27) (28) (29) If the component i Occupancy( x , y ), formula (27) if Indicates that if the component i Not a reagent j If the source or target component is a component of the same type, the reagent should pass through it before or after the component is occupied, that is, the time of occupation cannot overlap. if Indicates that if the component i For reagents j If the source component is a reagent, the reagent can only pass through after the component is occupied. if Indicates that if the component i If it is the target component of the reagent, the reagent cannot pass through except the input position in the upper right corner of the component.

[0061] In step S103, in actual industrial production, the solution time also needs to be considered as part of the total cost. Although the solution obtained by ILP is accurate, when using the ILP algorithm to solve, the CPU time will increase as the scale of the solution increases. Therefore, this application designs a pruning technology based on the timing graph structure to delete the above redundant formulas to speed up the solution time.

[0062] Pruning technology based on component relationships: like Figure 6 Taking the timing diagram shown in the figure as an example, formula (10) needs to judge S 1. S 2. S 3, so the constraint is expanded into 3 linear formulas. S 3 for operation S 1 and S 2's ancestor component must have , so we only need to consider S 1 and S2, the original constraint is pruned into a linear formula. The pruned formula (10) is as follows: (30) in, Representation Components i , k Are they grandparents and grandchildren? If not, their overlapping relationship needs to be constrained.

[0063] Fluid relationship-based pruning technology: like Figure 6 As shown, reagents 1 and 2 are the descendants of reagents 7 and 5, and formula (26) needs to constrain the conflict relationship between each reagent. However, reagents 7 and 5 will not be generated and transported until a period of time after the child reagents 1 and 2 have arrived at the destination, so only the conflict relationship between reagents 1 and 2 and reagents 3, 4, and 6 needs to be considered. The pruned formula (24) is as follows: (31) in, Reagent j and reagents e Are they grandparents and grandchildren? If not, their conflicting relationship needs to be restrained.

[0064] Pruning technology based on component fluid relationships: Formulas (27), (28) and (29) constrain the conflict relationship between components and reagents. Figure 6 As shown, according to the reusability of FPVA chips, components S 1After the execution is completed, the occupied FPVA grid cells will be restored to a reusable state, and the components S 1 The reagent 5 generated starts to move. At this time, the reagent 5 and the component S 1 does not have a conflict relationship, so formula (28) should be deleted. At the same time, reagent 7 and the ancestor component S 1. S 2. S 3 also does not need to judge the conflict relationship. Therefore, the formula after pruning is as follows: (32) (33) in, Representation Components i Is it a reagent? j ancestor component, if not, their conflict relationship needs to be constrained.

[0065] Fluid endpoint-based pruning techniques: Formula (16) and formula (22) constrain the reagent to not move further after reaching the target component. Formula (33) constrains the reagent to be prohibited from passing through the three directions of the remaining components except the target position. Therefore, formulas (14) and (20) are pruned, and the pruned formulas are as follows: (34) (35) Among them, the reagent to reach the target unit only needs to constrain the upper unit and the right unit, while the lower unit and the left unit are already constrained by formula (33).

[0066] In a specific embodiment, the present application uses multiple groups of standard test cases ranging from small to large to compare the design results before and after pruning, thereby verifying the effectiveness of the proposed pruning technology. In addition, it is also compared with the existing FPVA architecture comprehensive calculation to verify the overall efficiency of the method proposed in the present application.

[0067] The information of the 9 sets of smaller-scale standard test cases used in the experiment is shown in Table 2. Each set of test cases includes parameters such as the number of fluids, the number of mixing operations, and the array scale. The experiments were measured at 5×5 and 8×8 FPVA scales. The information of the larger-scale standard test cases used is shown in Table 3. The experiments were measured and verified at 8×8, 10×10, and 12×12 FPVA scales.

[0068] Table 29 Small-scale standard test cases

[0069] Table 311 Large-scale standard test cases

[0070] In order to verify the effectiveness of the proposed pruning technology, this application compares the design results before and after pruning under 9 smaller-scale test cases as shown in Table 2. The comparison results are shown in Table 4. It can be seen that the algorithm after using the pruning technology is 4.32 times faster than the algorithm before pruning in terms of CPU time. At the same time, the algorithms before and after pruning are compared under 11 larger-scale test cases as shown in Table 3. The comparison results are shown in Table 5. It can be seen that after pruning, the present application can obtain feasible solutions for all test cases within the set time, while the algorithm without pruning technology fails to obtain feasible solutions in the three test cases of IVD3, ProteinSplit3, and Synthetic3. And when the same feasible solution can be obtained before and after pruning, the algorithm after using the pruning technology is 4.43 times faster than the algorithm before pruning in terms of CPU time, which proves the effectiveness of the pruning technology.

[0071] Table 3 Comparison of CPU running time of algorithms before and after pruning under small-scale test cases

[0072] Table 5 Comparison of CPU running time of algorithms before and after pruning under large-scale test cases

[0073] In order to verify the overall performance of the method proposed in this application, the proposed method is further compared with the existing FPVA architecture comprehensive algorithm (using particle swarm algorithm layout and A* algorithm wiring), and the results are shown in Table 6. It can be seen that in the small-scale test case in Table 2, the total completion time of the biochemical experiment and the total length of the fluid transport path achieved 3.67% and 23.11% compression optimization respectively. In addition, the comparison results on the 11 larger-scale test cases in Table 3 are shown in Table 7. The method proposed in this application achieved 6.96% and 31.65% compression optimization in the total completion time of the biochemical experiment and the total length of the fluid transport path, respectively, further verifying the high efficiency of the performance of this application.

[0074] Table 6 Comparison results with existing FPVA chip architecture synthesis algorithms under small-scale test cases

[0075] Table 7 Comparison results with existing FPVA chip architecture synthesis algorithms under large-scale test cases

[0076] Through the above-mentioned architecture-integrated collaborative design method of microfluidic FPVA biochip, on the one hand, a high-order synthesis algorithm based on list scheduling is proposed, which can obtain the logical association between biochemical reagents and microfluidic components in a single scheduling process, significantly improving the design efficiency and quality of high-order synthesis. A physical collaborative design algorithm based on ILP is proposed, which takes into account the component layout and flow channel wiring, and generates high-quality physical design solutions with the goal of minimizing the total time to complete the biochemical experiment and the total length of the fluid transport path. The logical relationship between each operation and reagent in the timing diagram structure is used to screen the operations or reagents that need to be constrained, and then the redundant constraints are deleted to reduce the solution space size of the ILP model. On the other hand, the layout and wiring issues are taken into account, and the overall performance of the chip is significantly improved through integrated design and optimization, which reduces the total execution time of the biochemical experiment to the greatest extent, optimizes the total length of the fluid transport path, and reduces the CPU running time of the chip design.

[0077] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present disclosure, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0078] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0079] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A method for collaboratively designing an architecture of a microfluidic FPVA biochip, characterized in that: The method includes: A high-order synthesis algorithm based on list scheduling is used to obtain the logical association between biochemical reagents and microfluidic components in a single scheduling; With the goal of minimizing the total time to complete the biochemical experiment and the total length of the fluid transport path, an initial layout and routing scheme was designed using an ILP-based collaborative physical design algorithm; Based on the logical association between biochemical reagents and microfluidic components, the initial layout and routing scheme is optimized using the pruning technology based on the timing graph structure to obtain the target layout and routing scheme.

2. The method for collaboratively designing the microfluidic FPVA biochip architecture according to claim 1, characterized in that: The steps of obtaining the logical association between biochemical reagents and microfluidic components in a single scheduling by using a high-order synthesis algorithm based on list scheduling include: Read the data description of the timing diagram and convert it to obtain the basic structure diagram of the microfluidic component; Using the basic structure diagram, calculate the minimum delay schedule, and assign a start time and an end time to each schedulable component based on the minimum delay schedule; The components in the basic structure graph are scheduled using a high-order synthesis algorithm based on list scheduling. In the list scheduling process, all operations with an in-degree of 0 are first queued, and then iterated in the queue to schedule each dequeued operation and its successor operation, update its start time and end time, and queue the successor operation; Until the queue is empty, all operations have been scheduled. In the above process, the relationship between biochemical reagents and operations is recorded to generate a logical relationship array between biochemical reagents and microfluidic components.

3. The method for collaboratively designing the microfluidic FPVA biochip architecture according to claim 2, characterized in that: List-based scheduling based high-level synthesis algorithms include: Input mixed operands n , the operation set is ops [ s ], No. s The mixing time for each operation is Optime(o s ),operate s The start time is start_time s The operation ends at end_time s , the number of edges in the timing graph m , the edge set of the timing graph E ={ a , b };in, a and b Represents a sequence of operations: a arrive b ; The in-degree and out-degree of each operation are inDegree ( o s )and outDegree ( o s ), array v [ j ][ s ] indicates operation s Is it a reagent? j The destination, array out [ j ][ s ] indicates reagent j Whether it is operated s After completion, v [ j ][ s ]and out [ j ][ s ]The default values ​​are all 0; Iterate through each operation s Right now o s To check whether its degree is 0; if o s If the degree is 0, put it in the queue Q In the start_time x Set to 0; then, the association enters o x Reagents j With operation s Concurrently v [ j ][ s ] is 1, indicating o s For reagents j The target component; Iterating over the queue Q , retrieve the queue head o s , and set the end time of the operation end_time s , the formula is as follows: （1） in, Optime ( o s )express o s Operation time; get o s Subsequent operations o k , by setting v [ kin ][ k ] is 1 to associate the inflow operation k Reagents kin ; By out [ kout ][ k ] Set to 1 to associate the slave operation k Output reagents kout ; like o k is ready to be scheduled, then o k Join the queue Q ;in, o k The start time is updated according to the following formula: （2） in, p 1 for o k The first ancestor of p 2 for o k The second ancestor of p 3 for o k The third ancestor, r Time for fluid delivery.

4. The method for collaboratively designing the microfluidic FPVA biochip architecture according to claim 3, characterized in that: Aiming to minimize the total time to complete the biochemical experiment and the total length of the fluid transport path, the ILP-based collaborative physical design algorithm includes: objective functions, component constraints, and fluid constraints; Among them, component constraints include component direction coordinate uniqueness constraint, component coordinate direction constraint, component continuity constraint, component position relationship constraint, component conflict constraint, component end time constraint and component start time constraint; Fluid constraints include fluid starting point constraints, fluid end point constraints, fluid starting movement constraints, fluid end point movement constraints, fluid movement constraints, starting time constraints, fluid time constraints, reagent time relationship constraints, fluid starting point adjacent time constraints, fluid end point adjacent time constraints, fluid adjacent time constraints, fluid and coordinate axis constraints, inter-fluid constraints and component fluid constraints.

5. The method for collaboratively designing the microfluidic FPVA biochip architecture according to claim 4, characterized in that: The expression of the objective function is: （3） in, The first parameter is a value between 0 and 1. The second parameter is a value between 0 and 1. and Add up to 1; （4） （5） in, For reagents j The end time of O is a collection of components, N is a fluid collection, For reagents j Is it located in ( x , y ), W is the chip width, H is the chip height.

6. The method for collaboratively designing the microfluidic FPVA biochip architecture according to claim 5, characterized in that: Component direction coordinate unique constraint: Any direction of a component appears only once in the coordinate axis, and its constraint formula is: （6） in, For component collection O Components in i Is each direction occupied? x , y ), if type is 1, it means component collection O The lower left corner occupies the type 2, indicating a component collection O The lower right corner is occupied by type 3, indicating a component collection O The upper left corner occupies the space of the component. Type 4 indicates a component collection. O The upper right corner is occupied; Component coordinate direction constraint: Component type appears at any coordinate, and its constraint formula is: （7） Component continuity constraint: The position occupied by the component is a continuous piece, and its constraint formula is: （8） Component position relationship constraints: Components cannot occupy input and output ports. The constraint formula is: （9） Conflict constraints between components: The position relationship of components constrains the reuse of the same area between components at different times. The constraint formula is: （10） in, Arrival component for the first reagent i The time of the component i The placement time, For components k End time of Component end time constraint: The end time occupied by a component is the latest start time plus the operation time. The constraint formula is: （11） in, Components i End time of Component start time constraint: The earliest start time of a component is the arrival time of the first reagent, and the latest start time is the arrival time of the last reagent. The constraint formula is: （12） in, Arrival component for the last reagent i time.

7. The method for collaboratively designing the microfluidic FPVA biochip architecture according to claim 6, characterized in that: Fluid starting point constraint: Each reagent starts from its starting point, and the starting time of the reagent is when it is at the starting point. The constraint formula is: （13） in, Reagents j First arrival ( x , y ) time, For reagents j The start time, For reagents j Whether it is the final output fluid, For reagents j Is it a mixed component? i produce; Fluid endpoint constraint: Each reagent reaches a target position, and the end point is the end time of the reagent. The constraint formula is: （14） in, For reagents j The end time of For components i Is it a reagent? j The target component; Fluid initial movement constraint: If the reagent is at the starting position, then one of the adjacent coordinates of the starting position must be occupied. The constraint formula is: （15） Fluid endpoint movement constraint: If the reagent is at the target position, then one of the adjacent coordinates of the starting position must be occupied. The constraint formula is: （16） Fluid movement constraints: If the current reagent occupies ( x , y ) and is not at the starting position or has not reached the target position, the reagent will flow to the adjacent coordinates, and the constraint formula is: （17） Start time constraint: The start time of the input reagent is multiplied by the length of the reagent. The constraint formula is: （18） in, is the total length of the reagent; Fluid time constraint: The difference between the end time and the start time of the reagent is equal to the total length of the grid that the fluid passes through plus the length of the reagent itself. The constraint formula is: （19） Reagent time relationship constraint: the starting time of the reagent is equal to the corresponding component i The end time of is: （20） Fluid start point adjacent time constraints: （21） Fluid endpoint adjacent time constraints: （22） Fluid Adjacent Time Constraints: （23） Fluid and coordinate axis constraints: When a coordinate is occupied by a reagent, the time of the coordinate is enabled and recorded, and the constraint formula is: （24） （25） Inter-fluid constraint: During fluid transport, two reagents occupy the same grid cell in non-overlapping time. The constraint formula is: （26） Component fluid constraint: In the fluid transport path, if a non-target component is encountered, the reagent passes through before the component occupies the unit or after the component is executed. The constraint formula is: （27） （28） （29）。 8. The method for collaboratively designing the microfluidic FPVA biochip architecture according to claim 7, characterized in that: Pruning techniques based on timing graph structures include: Pruning technology based on component relationship, pruning technology based on fluid relationship, pruning technology based on component fluid relationship and pruning technology based on fluid endpoint; among them, Using the pruning technology based on component relationships, the conflict constraints between components are pruned to obtain: （30） in, For components i and components k Whether they are grandparents or grandchildren, if not, restrict their overlapping relationship; Using the pruning technology based on fluid relations, the constraints between fluids are pruned to obtain: （31） in, Reagent j and reagents e Whether they are grandparents and grandchildren, if not, restrict their conflicting relationship; Using the pruning technology based on component fluid relations, the component fluid constraints are pruned to obtain: （32） （33） in, Representation Components i Is it a reagent? j ancestor component, otherwise constrain its conflict relationship; Using the pruning technology based on fluid endpoint, the fluid endpoint constraint and reagent time relationship constraint are pruned to obtain: （34） （35）。

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