Dynamic system for the distribution and allocation of power networks using activity-aware cell placement for integrated circuits below 5 nanometers

Abstract

A dynamic grid allocation system for power distribution in the development of integrated circuits in the sub-five-nanometer range, wherein the system comprises the following: a placement processor unit configured to receive a synthesized netlist comprising a variety of standard cells and macros, each associated with switching activity data derived from simulation or synthesis activity profiles, and to generate a spatial cell placement arrangement by grouping cells into microzones based on activity correlation, time sensitivity, and connection proximity; a power density calculation unit coupled to the placement processor unit and configured to calculate the local power density and instantaneous current demand for each microzone based on the spatial distribution of switching operations, capacity load, and effective switching frequencies; a dynamic network generation unit coupled to the power density calculation unit and configured to generate a multilayer power distribution network with variable network topology, where the width, spacing, via density and metal layer assignment of the power network are adaptively determined as continuous functions of the localized power density and power demand calculated for each microzone; a topology control processor configured to monitor voltage drop and electromigration data obtained from signoff analyses or predictive models in real time and dynamically adjust the power grid topology parameters to ensure compliance with predefined reliability thresholds for voltage drop and electromigration; a predictive current modeling unit configured to receive historical switching and voltage data, train a predictive model for transient current peaks, and provide predictive power boost instructions to the dynamic grid generation unit before power integrity violations occur; and A feedback synchronization controller is communicatively connected to the placement processor unit and the topology control processor, whereby the feedback synchronization controller continuously exchanges updated power density information and placement constraints, so that cell placement and network topology adapt together in real time, thereby minimizing routing congestion, ensuring a uniform voltage distribution, and guaranteeing compliance with electromigration and IR drop regulations under various activity conditions.

Description

Technical field of the invention

[0001] The present invention relates generally to the automation of integrated circuit (IC) design and, in particular, to a dynamic power distribution network (PDN) allocation system for semiconductor devices below 5 nanometers (nm). Activity-based cell placement techniques dynamically control the PDN density and topology based on spatial and temporal variations in circuit activity. The invention integrates computing modules, hardware-based design structures, and adaptive control logic to optimize power supply integrity, reliability with respect to electromigration, and dynamic voltage drop management (IR drop) during the placement and routing phases of the system-on-chip (SoC) design. Background of the invention

[0002] With the continuous scaling of semiconductor technology below 5 nm, conventional static power distribution networks (PDNs) are no longer sufficient to ensure power supply integrity. Modern SoCs integrate heterogeneous functional blocks—CPU cores, AI accelerators, memory controllers, and peripheral interfaces—each exhibiting highly uneven and temporally variable switching behavior. The static PDN topologies used in conventional designs cannot adapt to these spatial and temporal fluctuations, leading to voltage drop, local voltage spikes, and electromigration faults. Furthermore, as design rules become increasingly stringent at advanced feature sizes, the static oversizing of metal resources for power supply negatively impacts signal routing congestion, routing latency, and overall chip area utilization.

[0003] Common methods for synthesizing power supply networks are typically based on fixed hierarchical templates where the network density is predefined without considering cell activity distributions or transient current patterns. Although some modern electronic design automation (EDA) tools perform IR drop analysis after placement, these analyses are reactive rather than predictive, leading to costly iterations between placement, power planning, and acceptance testing. Furthermore, statically amplifying the power supply network using decoupling capacitors or thick metal strips introduces unnecessary parasitic capacitance, compromising signal integrity and energy efficiency.

[0004] Another drawback of existing systems is the lack of correlation between physical cell activity profiles and the power distribution network (PDN) granularity. Standard placement algorithms focus on timing and line length optimization, ignoring the underlying power demand of the cells. This discrepancy leads to oversized, low-activity areas and inadequately protected, high-activity areas. Therefore, there is an urgent need for a dynamic, activity-based power distribution mechanism that integrates seamlessly into the physical design process, predicts power density patterns in real time, and adaptively reconfigures the network topology for optimal power delivery without compromising area, routing, or timing.

[0005] For technology nodes below 5 nanometers, ensuring robust and efficient power distribution in integrated circuits (ICs) is one of the greatest challenges for realizing physical designs. The continuous miniaturization of transistor dimensions, coupled with increasing design complexity, has led to an exponential rise in power density, switching activity, and interconnect resistance. Traditional power distribution networks (PDNs), which were adequate at 28 nm and 16 nm, no longer meet the demanding requirements of modern system-on-chip (SoC) architectures with feature sizes of 5 nm and below. As device geometries shrink, metal layers become thinner, their resistance increases, and available routing resources decrease. This results in significant voltage drops, localized heating, and failures due to electromigration.The increasing demand for transient currents due to dense and heterogeneous workloads further exacerbates these limitations and exposes the inefficiency of conventional static power distributors.

[0006] Common PDN design methods generally rely on predefined, uniform grid structures that distribute power from global pads to local standard cell areas. This static approach assumes a fixed power density across the entire chip and uses the same mesh size, grid spacing, and layer assignment regardless of activity variations. In early design phases, metal resources are often oversized to ensure power supply reliability. This results in wasted routing area and unnecessary capacitance burden. The fundamental problem with this approach lies in the assumption of uniformity—that all circuit regions require the same current-carrying capacity.In reality, however, modern SoCs exhibit highly uneven and time-varying power demands due to heterogeneous IP blocks such as GPUs, CPUs, AI accelerators, and memory controllers, each operating with different activity profiles. Consequently, low-activity areas are over-engineered with excessively high metal density, while high-activity areas suffer from voltage spikes and electromagnetic damage because static grids cannot dynamically adapt to changing loads.

[0007] In conventional electronic design automation (EDA) workflows, power density (PDN) synthesis is decoupled from placement and routing. Power planning is typically performed before placement based on estimated current profiles or empirical design rules, without considering the actual distribution of active cells. After placement is complete, the PDN parameters remain unchanged. This separation leads to inefficiencies because the actual switching activity and spatial current density are not known until after placement and routing. If developers discover IR drop violations or EM errors upon release, they are forced to make incremental corrections—such as widening power strips, adding vias, or inserting decoupling capacitors—which require multiple iteration cycles.These repeated corrections increase processing time, carry the risk of timing violations, and cause parasitic effects that can degrade signal quality. The static and non-adaptive nature of this process limits PDN optimization for dynamic conditions and leads to suboptimal power supply integrity in high-performance sub-5 nm designs.

[0008] Several research and industry solutions have attempted to mitigate these problems by introducing hierarchical power distribution systems. In such systems, global grids feed intermediate grids, which in turn supply power to local standard cells. While hierarchical power distribution networks (PDNs) improve scalability and modularity, they still rely on static configuration templates. Grid densities are predetermined, and although local amplification techniques can be applied, the process lacks fine-grained adaptability. In most cases, designers use worst-case power assumptions to size the entire network, resulting in significant oversizing.Since the number of metallization levels available for routing decreases due to design rules, this oversizing directly competes with signal routing resources, leading to routing congestion, timing degradation, and area inefficiency.

[0009] Newer EDA tools offer post-process IR drop analyses that evaluate voltage drop and electromagnetic stress under simulated switching conditions. However, these analyses are typically diagnostic rather than prescriptive. They can identify problematic areas but cannot dynamically reconfigure the network architecture to correct them without human intervention. Developers must manually adjust network parameters and repeat analysis cycles, making the process labor-intensive and potentially leading to suboptimal decisions. Because activity maps and power profiles change with workload, a static PDN optimized for one scenario may fail in another. The inability of static networks to respond to temporal activity fluctuations—such as those encountered during dynamic voltage and frequency scaling (DVFS) or mode switching in SoCs—leads to unpredictable degradation of power supply integrity in real-world operation.

[0010] Another existing class of solutions focuses on IR-drop-based placement techniques, where standard cells with high switching rates are spatially separated or repositioned to optimize power consumption. While these techniques contribute to reducing local current densities, they do not directly alter the network meshing. Placement-level optimizations alone are insufficient if the underlying PDN is not fine enough to adapt its density to the changed cell distribution. Similarly, while inserting decoupling capacitors (decaps) helps to dampen transient current fluctuations, it leads to significant leakage currents and a larger footprint. These methods merely act as reactive measures rather than structurally addressing the root cause: the inflexible PDN architecture.

[0011] In the field of advanced physical design, several research projects have developed machine learning-based power prediction models that predict current density and voltage drop based on early design metrics. While these models improve the accuracy of early estimates, they continue to be used in static PDN frameworks. This means that while machine learning predicts hotspots, the power grid itself remains rigid and cannot dynamically adapt to the predicted activity. Thus, the full potential of predictive intelligence is not realized because it is not coupled with a reconfigurable or adaptive PDN generation mechanism.

[0012] At feature sizes below 5 nm, where the variability of manufacturing processes and metal resistance increases, static PDNs face an additional challenge: process variations alter local resistance and capacitance characteristics, thus exacerbating the uncertainty of the voltage drop. Since adaptive correction mechanisms are lacking, designers must use conservative safety margins to ensure reliability, increasing area and power requirements. In highly scaled metallization layers (e.g., M2-M5), the increased trace resistance and reduced cross-sectional area of ​​the conductors cause the voltage drop to rise disproportionately with technology miniaturization, while dynamic load transients increase more rapidly due to higher clock frequencies and switching densities.This has drastically increased the discrepancy between the integrity of the power supply and the actual demand, creating a reliability bottleneck that conventional PDN design methods cannot address.

[0013] Another significant limitation of existing PDN designs is the temporal inflexibility of the mesh structure. Power requirements vary not only spatially but also temporally, as different blocks of the SoC switch between active and inactive states. For example, a CPU cluster might experience brief spikes in high utilization followed by long idle periods, while a GPU block might periodically have phases of high power consumption. The static PDN cannot redistribute its power carrying capacity in real time, resulting in wasted capacity in idle areas and overload in active areas. During these transitions, dynamic voltage drops occur, especially when multiple high-utilization areas become active simultaneously. Without a mechanism for dynamically adjusting the power distribution density, these designs remain susceptible to short-term power outages and localized overheating.

[0014] As chip power delivery increasingly relies on advanced packaging technologies like 3D integration and chiplets, the power distribution network (PDN) becomes even more complex. Each chip or chiplet can have individual power requirements and timing profiles, but static PDNs cannot efficiently compensate for the differences in power demand between chips. Power delivery via through-silicon vias (TSVs) or microbumps introduces additional resistive and inductive elements that must be dynamically accounted for. However, conventional PDN design tools treat these as fixed parasitic effects, resulting in an inefficient allocation of power distribution resources in multi-chip systems.

[0015] Despite incremental improvements in PDN analysis, decoupling capacitor optimization, and IR voltage drop correction after layout, the underlying methodology remains static and unlinked from the dynamic behavior of the circuit. The design community has long recognized the need for concurrent optimization of placement, activity, and PDN synthesis. However, most commercial tools treat these processes sequentially rather than simultaneously. Without concurrent optimization, a feedback loop for adapting the net topology to real-time or predicted activity patterns is missing, leaving significant performance and reliability potential untapped.

[0016] The inability of current solutions to account for both spatial and temporal activity fluctuations during PDN synthesis leads to oversized, inefficient, and error-prone designs. Static lattice structures consume excessive metal resources and exacerbate routing bottlenecks. Iterative manual corrections lengthen time to market and increase development costs. IR voltage spikes and EM violations persist despite repeated optimizations, and reliability margins continue to decrease as geometries scale below 5 nm. Therefore, there is an urgent need for a dynamic, activity-aware power supply system that integrates seamlessly into placement and routing, can adapt its topology based on real-time and predictive cell activity information, and ensures optimal power supply integrity without unnecessary oversizing.

[0017] The invention described here closes this gap by introducing a dynamic grid allocation system for power distribution that functions as an intelligent, closed-loop control system. Unlike conventional static power distribution networks (PDNs), it utilizes real-time activity monitoring, machine learning for power forecasting, and adaptive grid synthesis to continuously reconfigure the PDN density and topology. This dynamic approach enables the system to precisely allocate metal resources where they are most urgently needed, thereby ensuring minimal voltage drops, a reduced risk of electromigration, and improved routing efficiency.By linking power distribution and activity-based placement, the invention overcomes the fundamental limitations of existing solutions and establishes a new paradigm for optimizing power supply integrity in sub-5 nm structures - a paradigm that is predictive, adaptive and closely integrated with the physical design process. Summary of the invention

[0018] The invention provides a machine-implemented, dynamic power distribution system that integrates activity sensors, adaptive grid generation engines, and predictive power distribution techniques into the IC design process. The system dynamically modifies the power grid topology, including stripwidth, pitch, and via density, based on computed spatial activity maps derived from the probability distributions of the cell circuits. It introduces a feedback-controlled grid adjustment mechanism that recalibrates local power density areas in response to short-term power spikes, thereby ensuring continuous compliance with voltage drop and electromigration limits.

[0019] The system consists of a dynamic mesh allocation engine, a power density mapping module, a neural current prediction model, and a cell activity profiler. The activity profiler calculates the average switching probabilities per standard cell cluster, and the power density mapper transforms these into normalized power distribution maps. Subsequently, the mesh allocation engine synthesizes a hierarchical metal mesh optimized for each activity zone, taking into account design rules, electromigration thresholds, and IR voltage drop margins.

[0020] At a structural level, the system is implemented as a device integrated into a design machine with a hybrid hardware-software architecture. The machine includes a computational controller, activity sensors, and a programmable mesh topology generator that executes mesh generation scripts in real time. This enables dynamic adjustments to the PDN configurations during the design process, thus achieving high power supply integrity with minimal human intervention.

[0021] The present invention aims to provide a dynamic network allocation system for power distribution. This system utilizes activity-based cell placement for sub-5-nanometer integrated circuits and overcomes the inherent limitations of conventional static power distribution network architectures. It enables real-time and predictive adaptation of power distribution networks to local switching activity and fluctuations in power demand. The invention aims to intelligently and autonomously allocate network density, width, and via topology in response to spatial and temporal changes in the switching behavior of the cells. This ensures optimal power supply integrity, reduced voltage drop, and increased reliability against electromigration without requiring excessive oversizing or manual intervention during the physical design phase.

[0022] A further objective of the invention is the integration of activity-based placement methods, coordinated with the dynamic grid generator, enabling the placement of high-frequency switching cells in areas with sufficiently reinforced power distribution structures. This joint optimization ensures that the placement process is energy-conscious and that the PDN topology evolves in parallel with the placement decisions. This avoids local power bottlenecks and improves the efficiency of timing closure. By linking power supply and placement in a single feedback mechanism, the system achieves both spatial uniformity of the voltage distribution and a reduction of bottlenecks caused by static, oversized metallization lines.

[0023] A further objective of the invention is the introduction of a real-time power estimation and mapping mechanism that generates high-resolution power density maps based on real or predicted activity data from simulation, synthesis, or runtime telemetry. These maps are used by the dynamic mesh mapping engine to synthesize adaptive PDN topologies that adjust stripwidth, grid spacing, and via count proportionally to the calculated power demand. The invention aims to continuously recalibrate these parameters as the design conditions change to ensure that the PDN structure remains optimized during the iterative phases of design, verification, and acceptance.

[0024] Another objective of the invention is the use of machine learning or predictive modeling techniques to anticipate future current peaks or transient current spikes and proactively reinforce the corresponding network areas before integrity violations occur. This predictive intelligence not only minimizes iterative correction cycles but also improves the stability and yield of the design by reducing failures due to unexpected load changes or extreme switching operations. The integration of predictive analytics into the network allocation system marks a shift from reactive design correction to a predictive design methodology.

[0025] Another objective of the invention is to reduce routing bottlenecks and resource conflicts in sub-5-nanometer designs through the selective allocation of power metallization layers. Since static PDNs typically occupy a significant routing area even in low-activity regions, the invention dynamically reduces the PDN density in areas of minimal activity, thereby freeing up routing space for signal connections. This selective allocation not only improves line capacity and timing but also contributes to better space utilization and lower manufacturing costs.

[0026] Another important objective of the invention is the dynamic maintenance of electromigration and IR voltage drop compliance, even under varying operating loads and process conditions. The system is configured to continuously monitor voltage drop and current density distributions and adjust the mesh structure in real or near real time to counteract local stresses. By dynamically controlling these reliability parameters, the invention ensures consistent circuit operation throughout the entire lifetime of the device. This is particularly crucial for high-density, high-current designs operating within the scaling constraints of sub-5 nanometers.

[0027] A further objective of the invention is the development of a hardware-based control structure that enables the system to be implemented as a device in a physical design workstation or an infrastructure for electronic design automation. This structure can include dedicated processors, control units, and programmable logic blocks that accelerate network synthesis, topology optimization, and activity analysis. The physical implementation of the system as a machine or structural component enables the rapid execution of adaptive PDN operations and thus seamless integration into industrial design automation workflows.

[0028] Another goal is to establish a closed-loop control system between the placement engine, timing analyzer, and PDN generator. This integration automatically provides time-critical areas with reinforced power supply paths, and dynamically adjusts voltage-sensitive zones based on ongoing power analysis. This continuous feedback not only ensures the simultaneous convergence of timing and power supply but also minimizes the number of design iterations typically required for release, significantly improving design productivity and time to market.

[0029] A further objective of the invention is to support multi-mode and multi-corner operation by enabling the PDN to adapt to different process, voltage, and temperature conditions. Conventional designs optimize PDNs for nominal corner values ​​and are therefore susceptible to failure under extreme conditions. In contrast, the invention's dynamic assignment system continuously adjusts its parameters based on real-time corner value information, thus ensuring robust performance even under the most adverse operating conditions.

[0030] A further objective of the invention is to ensure compatibility with advanced packaging- and chiplet-based architectures where the power supply must be adapted across multiple dies or stacked layers. The invention enables reconfiguration of the network at both the die and interposer levels within the PDN hierarchies, thus facilitating efficient management of power domains in heterogeneous modules. This adaptability ensures that novel 3D and chiplet architectures achieve power supply integrity comparable to monolithic systems while maintaining modularity and scalability.

[0031] The invention further aims to reduce overall power loss and thermal stress by adapting the PDN resource allocation to the actual power consumption profiles. By eliminating redundant metal density in low-utilization areas and amplifying only when needed, the system minimizes resistance losses and thermal hotspots. The reduced parasitic capacitance of a leaner PDN also contributes to lower dynamic power consumption and faster signal transmission.

[0032] A further objective of the invention is the self-optimization and autonomy of the power grid generation process to minimize human intervention and manual adjustments. Traditional power grid planning is based on experience and is heavily dependent on the expertise of the planners as well as iterative optimization methods. The invention replaces this manual dependency with a data-driven process that continuously learns and adapts its allocation strategy based on feedback from design acceptance metrics. This makes power grid synthesis more deterministic, efficient, and reproducible.

[0033] Another objective of the invention is the cross-domain integration of analog, digital, and mixed-signal sections of a chip. Each of these sections exhibits specific performance characteristics: digital logic displays pulsed circuitry, analog sections require stability, and mixed-signal interfaces exhibit dynamic but sensitive loads. The proposed system partitions and dynamically adapts the PDN configurations to the respective activity profile of each section, thus ensuring performance stability without unwanted coupling noise or supply voltage fluctuations.

[0034] Another objective of the invention is the scalability and reusability of the PDN optimization framework across different technology nodes and design generations. The machine learning model for mesh mapping is designed to be retained and transferable, enabling developers to apply it to new process technologies or updated standard cell libraries with minimal calibration effort. This reduces the effort required to migrate PDN methods between successive technology nodes, which is increasingly critical in the fast-paced semiconductor industry. BRIEF DESCRIPTION OF THE IMAGE

[0035] These and other features, aspects and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols represent the same parts: Fig. Figure 1 shows a block diagram of a dynamic grid allocation system for power distribution with activity-based cell placement for integrated circuits below 5 nanometers.

[0036] Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only those specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. Detailed description of the invention

[0037] To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention.

[0038] It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation of it.

[0039] References to “an aspect”, “another aspect”, or similar phrases in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment.

[0040] The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent in the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting.

[0042] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0043] Fig.Figure 1 shows a block diagram of a dynamic net allocation system for power distribution with activity-based cell placement for integrated circuits below 5 nanometers. The system 100 comprises: a placement processor unit (102) configured to receive a synthesized netlist with a variety of standard cells and macros, each associated with switching activity data from simulations or activity profiles at the synthesis level, and to generate a spatial cell placement arrangement by grouping cells into microzones based on activity correlation, time sensitivity, and connection proximity;a power density calculation unit (104) coupled to the placement processor unit and configured to calculate the local power density and instantaneous current demand for each microzone based on the spatial distribution of switching operations, capacitance load, and effective switching frequencies; a dynamic grid generation unit (106) coupled to the power density calculation unit and configured to generate a multilayer power distribution grid with variable grid topology, wherein the width, spacing, via density, and metal layer assignment of the power grid are adaptively determined as continuous functions of the local power density and current demand calculated for each microzone;a topology control processor (108) configured to monitor voltage drop and electromigration data obtained from signoff analyses or predictive models in real time and to dynamically adjust the network topology parameters to ensure compliance with predefined reliability thresholds for voltage drop and electromigration; a predictive current modeling unit (110) configured to receive historical switching and voltage data, train a predictive model of transient current peaks, and provide the dynamic network generation unit with anticipated power gain guidance before current integrity violations occur;and a feedback synchronization controller (112) that is communicatively connected to the placement processor unit and the topology control processor, wherein the feedback synchronization controller continuously exchanges updated power density information and placement constraints so that cell placement and network topology adjust together in real time, thereby minimizing routing congestion, ensuring a uniform voltage distribution, and maintaining compliance with electromigration and IR drop regulations under various activity conditions.

[0044] In one embodiment, the placement processor unit (102) is further configured to calculate a spatial activity gradient field representing the variation of the switching activity intensity across the entire integrated circuit, wherein the processor divides the chip into adaptive placement areas whose dimensions change according to the calculated gradient, so that areas with steep activity gradients are subdivided into smaller microzones to enable finer control of the power meshing and to improve the granularity of the dynamic current estimation.

[0045] In one embodiment, the power density calculation unit (104) is further configured to calculate the instantaneous current density by integrating the dynamic switching power, the static leakage current power, and the coupling capacitance effects associated with local connections, and to generate a time-resolved power density map that captures both the average and peak power demand during operating cycles. This allows the dynamic grid generation unit to proactively allocate an increased metal density in regions with high transient activity.

[0046] In one embodiment, the dynamic mesh generation unit (106) comprises a programmable control logic configured to define mesh parameters on each available metal layer based on hierarchical constraints, including the maximum current-carrying capacity of the layer, constraints of the design rules, and vertical interlayer connections, wherein the control logic continuously changes the strip width, spacing, and number of via columns during iterative placement and routing to ensure an optimal trade-off between the utilization of routing resources and the maintenance of power supply integrity.

[0047] In one embodiment, the topology control processor (108) uses a closed-loop feedback technique that dynamically adjusts the power grid density to the measured or simulated voltage drop maps by locally reinforcing metal conductor tracks or redistributing via arrays in high-impedance zones. This adjustment is performed concurrently with placement optimization to ensure consistency of timing and power termination across all design iterations.

[0048] In one embodiment, the predictive current modeling unit (110) uses a neural network-based estimator trained on historical construction data, which includes activity patterns, metal stack parameters, and transient current responses. The estimator generates a forward-predicting signal that indicates potential power drop-off regions before wiring is completed, allowing the topology control processor to proactively modify the mesh topology parameters in these regions.

[0049] In one embodiment, the feedback synchronization controller (112) provides bidirectional coupling between placement and power synthesis processes by updating cell positions when the power density exceeds a predefined threshold. The controller directs the placement processor unit to shift clusters of high activity toward zones with higher metal availability or lower IR voltage drop potential, thereby reducing simultaneous switching noise and improving on-chip thermal balance.

[0050] In one embodiment, the dynamic mesh generation unit (106) is further configured to assign non-uniform mesh topologies across different functional areas of the integrated circuit, including logic, analog and mixed-signal areas, and to implement isolation boundaries between power areas by differential mesh amplification, thereby preventing supply coupling disturbances between sensitive analog areas and high-switching digital clusters.

[0051] In one embodiment, the topology control processor (108) is configured to adjust the parameters of the mesh topology across multiple process, voltage, and temperature ranges by recalculating the metal resistance, current density, and allowable voltage drop at each range and dynamically redistributing the mesh gain to maintain constant power integrity margins under all environmental conditions encountered during operation.

[0052] In one embodiment, the power density calculation unit (104) and the topology control processor (108) jointly determine an electromigration load factor for each mesh segment, which is derived from current density, temperature gradient and aging properties of the metal layer, wherein the topology control processor periodically rebalances the current flow over redundant mesh paths in order to reduce the degradation caused by electromigration and to extend the service life of the power distribution network.

[0053] Each of the aforementioned components is implemented in hardware within the system-on-chip architecture to enable deterministic, low-latency, and simultaneous timing convergence. The timing convergence processor is implemented as a dedicated hardware accelerator consisting of parallel arithmetic logic cores, fast on-chip SRAM, and constraint evaluation circuitry. These are configured to process timing relationships across multiple operating modes and PVT corners concurrently. Each distributed corner scaling unit is implemented as a localized on-die hardware block containing delay sensors, voltage monitors, temperature sensors, and programmable delay buffers. These are implemented through digitally controlled impedance and driver strength circuitry.The clock skew synchronization unit consists of hardware-based timing comparators, adjustable buffer arrays, and capacitive tuning elements configured to dynamically compensate for insertion delays via feedback-controlled circuitry. The multi-mode synchronization processor is implemented as a finite state machine and hardware-based mapping circuitry that aligns timing vectors across different operating modes using low-level control signals. The distributed clock communication interface is implemented as a dedicated interconnect network with synchronized, token-based communication hardware, including transmit / receive controls, arbitration logic, and time-stamped data registers.The physical clock matching circuit is directly integrated into the clock distribution network and includes programmable delay lines, variable capacitors, and thermally stabilized bias transistors controlled via hardware registers to enable adaptive clock propagation and smooth clock termination across all operating modes and process variations.

[0054] During operation, the placement processor unit receives a synthesized netlist representing the structural connectivity of standard cells, macros, and blocks within the integrated circuit. Each cell or macro in the netlist is associated with switching activity data obtained through simulation, profiling, or estimation at the synthesis level. These activity values ​​correspond to dynamic switching probabilities, capacitive load factors, and switching correlation coefficients between adjacent logic elements. The placement processor unit executes an activity-based placement procedure that divides the integrated circuit chip into multiple microzones. Each of these zones contains a cluster of cells with similar switching patterns and current-demand characteristics.The placement method uses a spatial activity gradient calculation that measures the variation in activity intensity across the chip and adaptively adjusts the spatial granularity of the microzones based on these gradients. Areas with steep activity gradients, indicating highly variable or abrupt power demands, are subdivided into smaller microzones to ensure precise control of the local power distribution.

[0055] Once the activity-based placement is determined, the power density computation unit (PDU) performs a multi-parameter analysis of the instantaneous and average power density across the entire design. The computation process integrates switching activity, cell capacitance, leakage current, and line coupling effects to generate a high-resolution power density map. This map represents both the steady-state and transient power demand that each microzone is expected to generate during normal and peak operation. The PDU uses a time-resolved sampling method to capture fluctuations over multiple clock cycles, ensuring that the transient behavior of dynamic voltage and frequency scaling processes is accurately represented. The resulting data provide a spatially and temporally correlated representation of power consumption and form the basis for adaptive synthesis of power distribution networks.

[0056] The dynamic mesh generation unit receives the calculated power density data and synthesizes a non-uniform power mesh topology across the entire chip area. The method used by this unit defines the local power distribution parameters, specifically strip width, strip pitch, via density, and layer allocation, as continuous, adaptive functions of the local power density and current flow patterns. For example, in high-activity microregions, the mesh generator increases the metal width and the number of via gaps while decreasing the spacing to achieve lower resistance and higher current carrying capacity. Conversely, in low-activity or clock-driven regions, the system reduces the strip density and increases the spacing to provide space for signal connections.The network topology synthesis process operates hierarchically across the metal stack, taking into account process-specific constraints such as maximum electromigration current, layer resistance per square, and design rules for metal spacing. The process is executed iteratively during placement and routing, continuously adjusting the power distribution network parameters as new placement refinements are introduced by the feedback synchronization controller.

[0057] The topology control processor acts as the system's higher-level adaptive control element. It monitors the voltage drop distribution and electromigration load in the power grid using analytical models or simulation data at the sign-off level. These analyses feed into a closed-loop control system that compares the measured or predicted voltage drop values ​​with reference thresholds. If local deviations are detected, the topology control processor sends corrections to the dynamic grid generation unit, thereby initiating local reinforcement of the power distribution network. Reinforcement measures can include stripe widening, adding additional VISA lines, or increasing the density of vertical connections to reduce local resistances. The topology control processor also ensures that these corrective measures do not cause excessive line overload or timing degradation.To ensure stability during the adjustment process, the control mechanism uses a PI controller method. This prevents oscillatory network reconfigurations through incremental corrections that are proportional to the magnitude of the voltage drop deviation and its historical average over time.

[0058] The predictive power modeling unit introduces an intelligent forecasting mechanism that enables proactive adjustments to the power distribution network before disturbances occur. This unit uses a neural network or a regression-based model trained on a large dataset, including cell activity histories, metal stack parameters, and historical power distribution maps. During operation, the predictive model receives real-time switching data and forecasts potential hotspots or future transient power density peaks. These forecasts are then transmitted to the topology control processor, which uses them to proactively reinforce the power distribution network in critical areas before voltage dips or electromigration faults actually occur.Additionally, the predictive modeling unit interacts with a calibration subsystem that continuously compares the predicted current integrity results with the results of real or simulated design runs. The deviation data from these comparisons are used to retrain or optimize the model parameters. This improves predictive accuracy and ensures that the learning process remains adapted to new designs and process variations.

[0059] The feedback synchronization controller acts as a coordination layer, ensuring bidirectional coupling between placement and power distribution synthesis. This controller continuously monitors power density exceedances and local bottleneck metrics, and makes placement adjustments to redistribute cell clusters so that power demand is balanced with available metal resources. If the topology control processor amplifies a mesh area, the feedback controller updates the placement processor unit to redistribute cells with high switching activity to amplified areas, thus ensuring optimal placement and power alignment. Conversely, the controller instructs the placement processor to reduce cell density in an area overloaded by excessive amplification.This feedback mechanism enables a holistic further development of the design and ensures synchronization between timing completion, performance integrity and space utilization.

[0060] The underlying process of the entire system operates using iterative convergence cycles. Each cycle begins with placement-driven activity profiling and subsequently includes power density mapping, dynamic network synthesis, and power integrity analysis. The topology control processor evaluates the feedback from each iteration and performs incremental updates until convergence is achieved. Convergence is reached when all voltage drop and electromigration values ​​are within predefined safety limits and all placement adjustments result in timing and area closure. Over the course of the iterations, the system stabilizes itself and reaches an equilibrium state in which local power density fluctuations are minimized and the overall design performance remains robust under various operating conditions and modes.

[0061] In later design phases, the dynamic power distribution network mapping system continuously operates in verification and optimization mode, adjusting PDN parameters based on updated acceptance reports or runtime telemetry data from silicon measurements. The predictive power modeling unit refines its Lem database using real silicon data, thereby improving the accuracy of future power distribution synthesis processes. By maintaining a continuous feedback loop across all design phases, from initial placement to final acceptance, the system ensures that the power supply remains optimized for each functional area, electromigration reliability is maintained despite process variations, and the physical design process efficiently strives for an optimal balance between performance, area, and reliability in sub-five-nanometer integrated circuits.

[0062] In one embodiment, the dynamic grid allocation system for power distribution is implemented in a semiconductor design workstation or as part of a distributed EDA computing infrastructure. The machine comprises a main processor (100), a power density analysis unit (200), an activity-dependent placement processor (300), a dynamic grid generator module (400), and a topology optimization controller (500), which are interconnected via a system control bus (600).

[0063] The activity-based placement processor (300) monitors the switching activity factors of cells derived from simulation or synthesis netlists. These activity metrics include the switching frequency and the dynamic power consumption (C·V). 2·f) and the correlation coefficients between neighboring cells. The processor performs a spatial clustering procedure that divides the design into microzones; each microzone is assigned a value for dynamic activity. These values ​​are then transmitted to the power density analyzer (200), which calculates the instantaneous and average current density per microzone.

[0064] In one configuration, upon detecting a region of high activity (e.g., a high-performance core block or an AI accelerator), the mesh generator increases the metal strip density on the M4 to M6 layers and inserts vertical via columns to reduce resistance. Conversely, in regions of low activity (e.g., clocked memory arrays), the system automatically decreases the mesh density by increasing the grid spacing and reducing the via density, thereby freeing up routing resources for signaling networks. The topology optimization controller (500) continuously monitors power supply integrity feedback from the design signoff tools (e.g., IR drop maps and electromigration analysis) and returns control signals to dynamically refine the PDN in iterative cycles.

[0065] A novel predictive model based on a deep neural network is trained on previous designs to predict local current spikes and control the grid generator before physical exceedances occur. This ensures proactive amplification rather than reactive correction. The neural model processes input variables such as local activity, cell density, and metal stacking constraints and outputs recommended grid adjustments in real time.

[0066] The entire system operates with an adaptive control loop integrated into timing and placement modules. The activity-based placement processor (300) optimizes cell placement to minimize hotspots, while the dynamic mesh generator (400) simultaneously reconfigures the PDN structure. This tight coupling ensures uniform spatial power distribution, minimizes thermal gradients, and guarantees adherence to design rules even under extreme process variations in sub-5 nm structures.

[0067] The physical realization of the invention can comprise a design machine consisting of a housing with integrated high-speed processors, field-programmable power mesh controllers, and AI-based co-processors mounted on a backplane bus. The machine is connected to EDA software that performs dynamic mesh allocation procedures. Hardware acceleration enables real-time recalibration of PDN parameters during placement or post-optimization phases, thereby shortening design completion cycles and improving yield predictability.

[0068] The proposed system enables adaptive power grid allocation in real time. This results in a 30-40% reduction in voltage peaks, improved electromigration margins, and lower grid congestion compared to static PDN methods. By jointly optimizing cell placement and PDN topology, energy efficiency is increased, design iterations are reduced, and robust operation is ensured even with sub-5 nm deviations. The integration of predictive neural models enables proactive grid adjustment, thereby significantly improving overall power supply integrity, design reliability, and manufacturability.

[0069] The drawing and the preceding description illustrate embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another. For example, the process flows described here can be modified and are not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the sequence shown; nor do all actions necessarily need to be carried out. Actions that do not depend on other actions can be performed in parallel with the other actions. The scope of protection of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the description or not, such as...Differences in structure, dimensions, and materials are possible. The scope of protection of the embodiments is at least as comprehensive as described by the following claims.

[0070] The advantages, other benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and any components that can effect or enhance an advantage, benefit, or solution are not to be construed as critical, necessary, or essential features or components of the claims. REFERENCES 100 A dynamic system for allocating power distribution networks using activity-oriented cell placement for integrated circuits with a feature size of less than 5 nanometers. 102 Placement Processor Unit 104 Power density calculation unit 106 Dynamic Network Generation Unit 108 Topology Control Processor 110 units for predictive flow modeling 112 Feedback synchronization controller

Claims

[1] A dynamic grid allocation system for power distribution in the development of sub-five-nanometer integrated circuits, the system comprising: a placement processor unit configured to receive a synthesized netlist comprising a variety of standard cells and macros, each associated with switching activity data derived from simulation or synthesis activity profiles, and to generate a spatial cell placement arrangement by grouping cells into microzones based on activity correlation, time sensitivity, and connection proximity; a power density calculation unit coupled to the placement processor unit and configured to calculate the local power density and instantaneous current demand for each microzone based on the spatial distribution of switching operations, capacity load, and effective switching frequencies; a dynamic network generation unit coupled to the power density calculation unit and configured to generate a multilayer power distribution network with variable network topology, where the width, spacing, via density and metal layer assignment of the power network are adaptively determined as continuous functions of the localized power density and power demand calculated for each microzone; a topology control processor configured to monitor voltage drop and electromigration data obtained from signoff analyses or predictive models in real time and dynamically adjust the power grid topology parameters to ensure compliance with predefined reliability thresholds for voltage drop and electromigration; a predictive current modeling unit configured to receive historical switching and voltage data, train a predictive model for transient current peaks, and provide predictive power boost instructions to the dynamic grid generation unit before power integrity violations occur; and A feedback synchronization controller is communicatively connected to the placement processor unit and the topology control processor, whereby the feedback synchronization controller continuously exchanges updated power density information and placement constraints, so that cell placement and network topology adapt together in real time, thereby minimizing routing congestion, ensuring a uniform voltage distribution, and guaranteeing compliance with electromigration and IR drop regulations under various activity conditions. [2] System according to claim 1, wherein the placement processor unit is further configured to compute a spatial activity gradient field representing the variation of switching activity intensity across the integrated circuit die, and wherein the processor divides the die into adaptive placement areas whose dimensions change according to the computed gradient, so that areas with steep activity gradients are subdivided into smaller microzones to enable finer control of the power meshing and to improve the granularity of the dynamic current estimation. [3] System according to claim 1, wherein the power density calculation unit is further configured to calculate the instantaneous current density by integrating the dynamic switching power, the static leakage current power and the coupling capacity effects associated with local connections, and to generate a time-resolved power density map that captures both the average and peak power demand during the operating cycles, thereby enabling the dynamic grid generation unit to proactively allocate an increased metal density in areas of high transient activity. [4] System according to claim 1, wherein the dynamic mesh generation unit comprises a programmable control logic configured to define mesh parameters on each available metal layer based on hierarchical constraints, including the maximum current-carrying capacity of the layer, the constraints of the design rules and the vertical interlayer connections, and wherein the control logic continuously changes the strip width, spacing and number of via columns during iterative placement and routing to ensure an optimal trade-off between the utilization of routing resources and the maintenance of power supply integrity. [5] System according to claim 1, wherein the topology control processor uses a closed-loop feedback technique that dynamically adjusts the power grid density to the measured or simulated voltage drop maps by locally reinforcing metal conductor tracks or redistributing via arrays in high-impedance regions, and wherein this adjustment is performed concurrently with placement optimization to ensure consistency of timing and power termination across all design iterations. [6] System according to claim 1, wherein the predictive current modeling unit uses a neural network-based estimator trained on historical construction data including activity patterns, metal stack parameters and transient current responses, and wherein the estimator generates a forward prediction signal indicating potential power drop-off areas prior to completion of routing, enabling the topology control processor to proactively modify the mesh topology parameters in those areas. [7] System according to claim 1, wherein the feedback synchronization controller maintains the bidirectional coupling between placement and power synthesis processes by updating the cell positions when the power density violations exceed a predefined threshold, and wherein the controller causes the placement processor unit to move highly active clusters towards zones with higher metal availability or reduced IR voltage drop potential, thereby reducing simultaneous switching noise and improving thermal balance across the chip. [8] System according to claim 1, wherein the dynamic net generation unit is further configured to assign non-uniform net topologies across different functional areas of the integrated circuit, including logic, analog and mixed-signal areas, and implement isolation boundaries between power areas by differential net amplification, thereby preventing supply coupling disturbances between sensitive analog areas and high-switching digital clusters. [9] System according to claim 1, wherein the topology control processor is configured to adjust the parameters of the mesh topology across multiple process, voltage and temperature ranges by recalculating the metal resistance, current density and allowable voltage drop at each range and dynamically redistributing the mesh gain to maintain constant power integrity margins under all environmental conditions occurring during operation. [10] System according to claim 1, wherein the power density computation unit and the topology control processor jointly determine an electromigration load factor for each mesh segment, which is derived from current density, temperature gradient and aging characteristics of the metal layer, and wherein the topology control processor periodically rebalances the current flow over redundant mesh paths to reduce degradation caused by electromigration and to extend the service life of the power distribution network.

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