In this article, we propose a method for sizing an arbitrary combinational datapath to minimize its energy consumption. Our method involves deriving expressions for the components of energy consumption at both the stage and path levels. In this work, we identify overshoot energy ($E_{\text {OS}}$) consumption as a previously unreported component contributing to energy consumption, particularly significant in the near/sub-threshold voltage regime. We determine that this$E_{\text {OS}}$consumption is proportional to the input and output transition times and size of a logic gate at a particular stage of a datapath. We also observe that, for a given number of stages (N) and path effort, the total energy consumption is optimized when the stage effort (f) in a datapath is kept constant. Based on our observations and derivations of all the energy components and the requirement for a constant “f” in the datapath, we develop a method to minimize the energies of a logic circuit while maintaining the timing closure requirement. We determine that the non-critical paths (NCPs) must be sized to a minimum “f” while maintaining the timing requirements. We verified our models on several ISCAS and EPFL benchmark circuits with an average reduction of 28.1% (41.2%) and 19.2% (28.4%) in energy consumption [figure of merit (FoM)], respectively. The proposed methodology predicts the total energy consumption at a stage and path level of N-stage logic, with only one-time SPICE simulation on a single stage, with a maximum error of 1.3% and 1.62%, respectively, against SPICE simulations. The simulations are performed in Synopsys HSPICE environment with ST Microelectronics 65 nm CMOS and 28 nm FDSOI technology nodes, resulting in a very good agreement with the developed methodology.

Building upon recent advances in analog electronic design automation (EDA), this paper discusses directions for reinforcing the connection between design and EDA, in order to develop solutions that are meaningful to designers. Two aspects, both related to bridging the gap between EDA and designers, are highlighted. The first discusses the use of test structures to generate meaningful characterized data to aid design automation, specifically understanding the impact of random, correlated, and systematic variations on the design of matched structures. Results on a recent test chip that analyzes these variations and their impact on EDA design choices will be presented. The second illustrates a design testcase that applies analog EDA techniques, using the ALIGN layout engine, to design an RF MIMO receiver, and describes how this experience has helped both in advancing the state of analog EDA and in building circuits with enhanced designer productivity.

We propose switching activity factor-based effective current source model (SAFE) for aging-aware static timing analysis (STA), a new technique for estimating the timing performance of digital circuits. SAFE is based on the development of device-level variation-aware analytical timing models of stacked and multistage logic cells (commonly employed transistor topologies in a synthesized netlist of a random logic path), which drastically reduces the recharacterization efforts of the standard cells. The models developed are derived as a function of input transition time$(T_{R})$and load capacitance$(C_{L})$. The timing performance of a standard cell degrades with threshold voltage$(V_{\mathrm {th}})$degradation in a MOS device due to various aging mechanisms. SAFE, makes the entire STA process aging aware by updating its model coefficients with$V_{\mathrm {th}}$degradation caused by aging. It is achieved by proposing a method for estimating$V_{\mathrm {th}}$degradation under various stress conditions, including static, dynamic, and asymmetric, that applies to any process design kit (PDK). To consider asymmetric aging, we have developed a method to find effective switching activity factor$(\alpha _{\mathrm {eff}})$for N-stage stacked and N-stage parallel logic which is used to find the value of switching activity factor$(\alpha)$at intermediate nodes in pipelined logic circuits. Our simulations are performed in Mentor Graphics Eldo SPICE environment using STMicroelectronics 28 and 65-nm CMOS process. The proposed technique provides a high-simulation accuracy (2.5% average error) when compared with SPICE simulations. Finally, we achieved a ~98.14% reduction in the required number of simulations using SAFE when compared with a completely SPICE/Aging simulation-based approach.

The design of active array structures in analog circuits requires careful matching to minimize the impact of variations. This work presents a constructive approach for building these arrays to directly incorporate shifts due to process variations, considering systematic first-order and second order gradients; to account for systematic layout effects, including parasitic mismatch and layout-dependent effects due to stress; and to ensure that the resulting layout delivers high performance. The proposed algorithms are targeted to FinFET technologies and are validated for multiple analog blocks in a commercial 12nm FinFET process. The layouts generated by the proposed method are demonstrated to provide better matching and performance than prior methods.

Recent advances in auto-generating analog and mixed-signal (AMS) circuits use standard digital tool flows to compose AMS circuits from a combination of digital standard cells and a set of auxiliary cells (auxcells). Until now, generating auxcell layouts for each new PDK was the last manual step in the flow for auto-generating AMS components, which limited the available auxcells and reduced the optimality of the auto-generated AMS designs. To solve this, we propose AuxcellGen, a framework to auto-generate auxcell layouts and performance models. Aux-cellGen generates a parasitic-aware auxcell performance model using a neural network (NN), auto-sizes and optimizes auxcell schematics for a given design target, and auto-generates auxcell layouts. The framework is demonstrated by auto-generating tristate buffer auxcells for PLLs and sense-amplifier auxcells for SRAM across a range of user specifications that are compatible with standard cell and memory bitcell pitch.

Abstract Digital images are more important in numerous contemporary applications, and the need for images in the technical field is also increasing drastically. It is used to recognize signatures and faces in many industries and is applicable for intelligent departments. The images are usually associated with the noise content; this may happen due to the instrument imperfections, troubleshooting while collecting data from the acquisition process, and another natural phenomenon. Poisson noise, also known as photon noise, is caused in the images due to the statistical essence of electromagnetic waves. X‐ray, visible light, and gamma rays are electromagnetic waves. The enhancement of the convolution model in addressing images is challenging due to the various constituents such as optical aberrations, noise level, and optical setup. The modeling configuration of the image is attained using the point spread function (PSF), which is responsible for the system's impulse response. The quality image is retrieved by denoising and super‐resolution (SR) methods; these methods simultaneously eliminate the noise content from the images. A Richardson–Lucy and alternating direction method of multipliers type of non‐blind iterative algorithmic approaches associated with the PSF performance in addressing image is comparatively analyzed. The deep learning approach, convolutional neural networks (CNNs), is also employed to understand the nonlinear mapping relationship between the observed data and ground reality. The performance of the various network approaches is compared in this article. The result obtained shows that the deep learning CNNs achieved higher accuracy in producing denoising images. The goal of the proposed system model is to remove the interference noise in images. The high‐resolution images are obtained by implementing a SR‐based CNN model.

A static timing analysis (STA) methodology based on an effective current source model (ECSM) is proposed for the first time for estimating the aging-aware path-level timing performance and its impact on the logical effort of a CMOS inverter for digital timing closure in pre-stress and post-stress conditions. Degradation in the threshold voltage$(V_{\mathrm{ th}})$of PMOS occurs due to temporal variability mechanisms (aging), such as negative bias temperature instability, resulting in delay degradation of a standard cell. Therefore, we proposed a technique to make the STA process aware of this degradation by developing device-level variation aware (with aging) timing models of CMOS inverters to represent threshold-crossing points (TCPs) in an ECSM.libs file as a function of stress time ($t$). A device-level approach for$V_{\mathrm{ th}}$degradation into different aging conditions, such as static and dynamic, is developed for a given process design kit to update TCPs in a (.libs) file as a function of$t$. A python-based tool is being developed to estimate the path-level timing performance of digital circuits in pre- and post-stress conditions. Again, we developed a technique for relating the inverter’s logical effort with$t$to resize a near-critical path in pre-stress conditions for achieving digital timing closure in pre- and post-stress conditions. The verification and validation of the proposed model with different benchmark circuits are performed using a parasitic extracted netlist in the Eldo SPICE environment with the 65-nm CMOS process technology. Finally, our model reduces the number of SPICE/Stress simulations by 98.13% compared to the previously reported only simulation-based techniques.

Process variations and the effect of interconnect parasitics can cause significant perturbations in the performance metrics of capacitive digital-to-analog converters (DACs). This article develops fast constructive procedures for common-centroid placement and routing for binary-weighted and split capacitor array topologies of charge-sharing DACs. Our approach particularly targets FinFET technologies with high wire and via parasitics: in these technology nodes, we show that the switching speed of the capacitor array, as measured by the 3-dB frequency, can be severely degraded by these parasitics, and develop techniques to place and route the capacitor array, for both binary-weighted and split DACs, to optimize the switching speed. A balance between 3-dB frequency and DAC INL/DNL is shown by trading off via counts with dispersion. The approach delivers high-quality results with low runtimes.

Analog designs consist of multiple hierarchical functional blocks. Each block can be built using one of several design topologies, where the choice of topology is based on circuit performance requirements. A major challenge in automating analog design is in the identification of these functional blocks, which enables the creation of hierarchical netlist representations. This can facilitate a variety of design automation tasks, such as circuit layout optimization, because the layout is dictated by constraints at each level, such as symmetry requirements, that depend on the topology of the hierarchical block. Traditional graph-based methods find it hard to automatically identify the large number of structural variants of each block. To overcome this limitation, this article leverages recent advances in graph neural networks (GNNs). A variety of GNN strategies is used to identify netlist elements for circuit functional blocks at higher levels of the design hierarchy, where numerous design variants are possible. At lower levels of hierarchy, where the degrees of freedom in circuit topology is limited, structures are identified using graph-based algorithms. The proposed hierarchical recognition scheme enables the identification of layout constraints, such as symmetry and matching, which enable high-quality hierarchical layouts. This method is scalable across a wide range of analog designs. An experimental evaluation shows a high degree of accuracy over a wide range of analog designs, identifying functional blocks, such as low-noise amplifiers, operational transconductance amplifiers, mixers, oscillators, and band-pass filters, in larger circuits.

Well island generation and well tap placement is an important problem in analog/mixed-signal (AMS) circuits. Well taps can only prevent latchups within a certain radius of influence within a well island, and hence must be appropriately inserted to cover all devices. However, existing automated AMS layout paradigms typically defer the insertion of well taps and creation of well islands to a post-processing step after placement. This alters the placement, resulting in increased area and wire length, as well as circuit performance degradation. Therefore, there is a strong need for a solution that generates well islands and inserts well taps during placement so the placer can account for well overheads in optimizing placement metrics. In this work, we propose a modular solution using a graph-based optimization scheme that can be used within multiple placement paradigms with minimal intrusion. We demonstrate the integration of this scheme into stochastic, analytical, and designer-driven row-based placement. The method is demonstrated in advanced FinFET technologies. Layouts generated using this scheme show better area, wire length, and performance metrics at the cost of a marginal runtime degradation when compared to the post-processing approach. Using our scheme, there is an average improvement of 3% and 4% and a maximum improvement of 23% and 11% in area and wirelength, respectively, of layouts of various classes of AMS circuits at the cost of 17% average and 29% maximum increase in total runtime.

Analog IC performance has a strong dependence on interconnect RC parasitics, which are significantly affected by wire sizes in recent technologies, where minimum-width wires have high resistance. However, performance-driven wire sizing for analog ICs has received very little research attention. In order to fill this void, we develop several techniques to facilitate an end-to-end automatic wire sizing approach. They include a circuit performance model based on customized graph neural network (GNN) and two optimization techniques: one using Bayesian optimization accelerated by the GNN model, and the other based on TensorFlow training. Experimental results show that our technique can achieve 11% circuit performance improvement or 8.7× speedup compared to a conventional Bayesian optimization method.

This paper presents an overview of common-centroid (CC) layout styles, used in analog designs to overcome the impact of systematic variations. CC layouts must be carefully engineered to minimize the impact of mismatch. Algorithms for CC layout must be aware of routing parasitics, layout-dependent effects (for active devices), and the performance impact of layout choices. The optimal CC layout further depends on factors such as the choice of the unit device and the relative impact of uncorrelated and systematic variations. The paper also examines scenarios where non-CC layouts may be preferable to CC layouts.

The accuracy and linearity of capacitive digital-to-analog converters (DACs) depend on precise capacitor ratios, but these ratios are perturbed by process variations and parasitics. This paper develops fast constructive procedures for common-centroid placement and routing for binary-weighted capacitors in charge-sharing DACs. Parasitics also degrade the switching speed of a capacitor array, particularly in FinFET nodes with severe wire/via resistances. To overcome this, the capacitor array is placed and routed to optimize switching speed, measured by the 3dB frequency. A balance between 3dB frequency and DAC INL/DNL is shown by trading off via counts with dispersion. The approach delivers high-quality results with low runtimes.

Well island generation and well tap placement pose an important challenge in automated analog/mixed-signal (AMS) layout. Well taps prevent latchup within a radius of influence in a well island, and must cover all devices. Automated AMS layout flows typically perform well island generation and tap insertion as a postprocessing step after placement. However, this step is intrusive and potentially alters the placement, resulting in increased area, wire length, and performance degradation. This work develops a graph-based optimization that integrates well island generation, well tap insertion, and placement. Its efficacy is demonstrated within a stochastic placement engine. Experimental results show that this approach generates better area, wire length and performance metrics than traditional methods, at the cost of a marginal runtime degradation.

Placement algorithms for analog circuits explore numerous layout configurations in their iterative search. To steer these engines towards layouts that meet the electrical constraints on the design, this work develops a fast feasibility predictor to guide the layout engine. The flow first discerns rough bounds on layout parasitics and prunes the feature space. Next, a Latin hypercube sampling technique is used to sample the reduced search space, and the labeled samples are classified by a linear support vector machine (SVM). If necessary, a denser sample set is used for the SVM, or if the constraints are found to be nonlinear, a multilayer perceptron (MLP) is employed. The resulting machine learning model demonstrated to rapidly evaluate candidate placements in a placer, and is used to build layouts for several analog blocks.

Hierarchical analog layout generators proceed from leaf cells (“primitives”) to progressively larger blocks that are placed and routed. The quality of primitive cell layout is critical for design performance. This paper proposes a methodology that defines and optimizes the performance metrics of primitives during leaf cell layout. It incorporates layout parasitics and layout-dependent effects, providing a set of optimized layout choices for use by the place-and-route engine, as well as wire sizing guidelines for connections outside the cell. For FinFET-based designs of a high-frequency amplifier, a StrongARM comparator, and a fully differential VCO, our approach outperforms existing methods and is competitive with time-intensive manual layout.

Common-centroid (CC) layouts are widely used in analog design to make circuits resilient to variations by matching device characteristics. However, CC layout may involve increased routing complexity and higher parasitics than other alternative layout schemes. This paper critically analyzes the fundamental assumptions behind the use of common-centroid layouts, incorporating considerations related to systematic and random variations as well as the performance impact of common-centroid layout. Based on this study, conclusions are drawn on when CC layout styles can reduce variation, improve performance (even if they do not reduce variation), and when non-CC layouts are preferable.

We propose a complete analog mixed-signal circuit design flow from specification to silicon with minimum human-in-the-loop interaction, and verify the flow in a 12nm FinFET CMOS process. The flow consists of three key elements: neural network (NN) modeling of the parameterized circuit component, a search algorithm based on NN models to determine its sizing, and layout automation. To reduce the required training data for NN model creation, we utilize transfer learning to improve the NN accuracy from a relatively small amount of post-layout/silicon data. To prove the concept, we use a voltage-controlled oscillator (VCO) as a test vehicle and demonstrate that our design methodology can accurately model the circuit and generate designs with a wide range of specifications. We show that circuit sizing based on the transfer learned NN model from silicon measurement data yields the most accurate results.

The common-centroid (CC) layout style is widely used to minimize the impact of variations among matched devices in analog blocks such as current mirror banks and differential pairs. This paper presents a constructive, performance-aware CC placement and routing algorithm for transistor arrays. Specifically, the proposed approach maximizes diffusion sharing, incorporates length of diffusion (LOD) based stress-induced performance variations, and mitigates resistive parasitics and electromigration (EM) hotspots, all of which are critical in modern technology nodes. The proposed algorithms are validated using cell- and circuit-level test cases in a commercial 12nm FinFET process. As compared to existing works, the cells generated using the proposed approach are shown to provide better performance in the presence of systematic variations, LOD, layout parasitics, and EM-induced degradation.

The quality of layouts generated by automated analog design have traditionally not been able to match those from human designers over a wide range of analog designs. The ALIGN (Analog Layout, Intelligently Generated from Netlists) project [2, 3, 6] aims to build an open-source analog layout engine [1] that overcomes these challenges, using a variety of approaches. An important part of the toolbox is the use of machine learning (ML) methods, combined with traditional methods, and this talk overviews our efforts. The input to ALIGN is a SPICE-like netlist and a set of perfor- mance specifications, and the output is a GDSII layout. ALIGN automatically recognizes hierarchies in the input netlist. To detect variations of known blocks in the netlist, approximate subgraph iso- morphism methods based on graph convolutional networks can be used [5]. Repeated structures in a netlist are typically constrained by layout requirements related to symmetry or matching. In [7], we use a mix of graph methods and ML to detect symmetric and array structures, including the use of neural network based approximate matching through the use of the notion of graph edit distances. Once the circuit is annotated, ALIGN generates the layout, going from the lowest level cells to higher levels of the netlist hierarchy. Based on an abstraction of the process design rules, ALIGN builds parameterized cell layouts for each structure, accounting for the need for common centroid layouts where necessary [11]. These cells then undergo placement and routing that honors the geomet- ric constraints (symmetry, common-centroid). The chief parameter that changes during layout is the set of interconnect RC parasitics: excessively large RCs could result in an inability to meet perfor- mance. These values can be controlled by reducing the distance between blocks, or, in the case of R, by using larger effective wire widths (using multiple parallel connections in FinFET technologies where wire widths are quantized) to reduce the effective resistance. ALIGN has developed several approaches based on ML for this purpose [4, 8, 9] that rapidly predict whether a layout will meet the performance constraints that are imposed at the circuit level, and these can be deployed together with conventional algorithmic methods [10] to rapidly prune out infeasible layouts. This presentation overviews our experience in the use of ML- based methods in conjunction with conventional algorithmic ap- proaches for analog design. We will show (a) results from our efforts so far, (b) appropriate methods for mixing ML methods with tra- ditional algorithmic techniques for solving the larger problem of analog layout, (c) limitations of ML methods, and (d) techniques for overcoming these limitations to deliver workable solutions for analog layout automation.

Automated subcircuit identification and annotation enables the creation of hierarchical representations of analog netlists, and can facilitate a variety of design automation tasks such as circuit layout and optimization. Subcircuit identification must navigate the numerous alternative structures that can implement any analog function, but traditional graph-based methods cannot easily identify the large number of such structural variants. The novel approach in this paper is based on the use of a trained graph convolutional neural network (GCN) that identifies netlist elements for circuit blocks at upper levels of the design hierarchy. Structures at lower levels of hierarchy are identified using graph-based algorithms. The proposed recognition scheme organically detects layout constraints, such as symmetry and matching, whose identification is essential for high-quality hierarchical layout. The subcircuit identification method demonstrates a high degree of accuracy over a wide range of analog designs, successfully identifies larger circuits that contain subblocks such as OTAs, LNAs, mixers, oscillators, and band-pass filters, and provides hierarchical decompositions of such circuits.

Automating analog layout is a long-standing research problem, with a history that goes back several decades. While digital design is largely automated today, analog layout has been significantly more resistant: automation has not made much headway in industry settings. There are several reasons for this, including:

Analog IC placement is typically a manual process that requires strong experience and trial-and-error iterations as it produces a large impact to circuit performance in a complicated manner. Although automatic analog placement has been studied for decades, existing methods are inadequate for achieving performance comparable with manual designs. In this work, a customized graph neural network model is developed for predicting the impact of placement on circuit performance. Knowledge obtained by such a model can be transferred among different topologies of the same circuit type. Simulation results show that the proposed model is superior to a recent CNN-based work in terms of both accuracy and knowledge transfer. It also outperforms a plug-in use of graph attention network. The proposed model is further applied in analog IC placement and achieves performance similar to manual designs.

The problem of analog design automation has vexed several generations of researchers in electronic design automation. At its core, the difficulty of the problem is related to the fact that machinegenerated designs have been unable to match the quality of the human designer. The human designer typically recognizes blocks from a netlist and draws upon her/his experience to translate these blocks into a circuit that is laid out in silicon. The ability to annotate blocks in a schematic or netlist-level description of a circuit is key to this entire process, but it is a process fraught with complexity due to the large number of variants of each circuit type. For example, the number of topologies of operational transconductance amplifiers (OTAs) easily numbers in the hundreds. A designer manages this complexity by dividing this large set of variants into classes (e.g., OTAs may be telescopic, folded cascode, etc.). Even so, the number of minor variations within each class is large. Early approaches to analog design automation attempted to use rule-based methods to capture these variations, but this database of rules required tender care: each new variant might require a new rule. As machine learning (ML) based alternatives have become more viable, alternative forms of solving this problem have begun to be explored.

This paper presents analog layout automation efforts under the ALIGN ("Analog Layout, Intelligently Generated from Netlists") project for fast layout generation using a modular approach based on a mix of algorithmic and machine learning-based tools. The road to rapid turnaround is based on an approach that detects structure and hierarchy in the input netlist and uses a grid based philosophy for layout. The paper provides a view of the current status of the project, challenges in developing open-source code with an academic/industry team, and nuts-and-bolts issues such as working with abstracted PDKs, navigating the "wall" between secured IP and open-source software, and securing access to example designs.

Near-threshold voltage (NTV) digital VLSI circuits, though important, have their sequential elements vulnerable to soft errors. The critical charge for a single event upset for a D-latch depends on its fan-out load, supply voltage, and transistor level parameters. A SPICE simulation-based estimation of the critical charge is highly resource/time intensive. In this paper, we propose a physics-based semianalytical model to estimate the critical charge of a static D-latch as a function of its fan-out load, supply voltage, temperature, and transistor levels parameters. It can, therefore, be used while considering process voltage temperature (PVT) variations. The critical charge estimated by the model is in good agreement with SPECTER simulations with a maximum error of less than 3.4% employing STMicroelectronics 65-nm process design kit (PDK). We also validated the model at 32-nm technology node using technology computer-aided design (TCAD) mixed-mode simulations (a maximum error of less than 7.5% is observed). Using this model, we devise a methodology to estimate the critical charge using a few dc simulations and a single transient SPICE simulation for a given PDK. This is an end-to-end method to include an accurate estimation of the critical charge for latches in NTV standard cell library characterization.