FinFET, now firmly established in leading VLSI industries for their superior performance, exhibit heightened aging susceptibility that poses significant reliability challenges. The aggressive scaling of technology nodes has further compromised circuit reliability in recent years, highlighting the need for effective aging mitigation techniques. Recent advancements in the miniaturization of nanoscale technology have demonstrated the potential of optimizing performance parameters in standard cells using machine learning models and optimization algorithms through device sizing modifications. Building on this progress, we propose a methodology for optimizing performance parameters in 16nm high-performance (HP) FinFET for the first time. The approach leverages a multi-objective optimization algorithm framework to mitigate aging impacts across PVT variations while addressing NBTI and HCI effects by optimally adjusting FinFET design parameters, including channel length (lg), width (tfin), and height (hfin). With SPICE simulations, time-series datasets were generated to train machine learning models that achieved an R2 score exceeding 0.99 and a mean absolute percentage error below 1% across standard cells. Our approach yields a significant simulation speedup and a reduction in simulation workload compared to traditional SPICE simulations. Using the proposed optimization algorithm framework, we improved the power-delay product (PDP) by up to 36.97% under non-aging conditions and 34.94% with aging considered with respect to the nominal dimension at the fresh year, demonstrating significant performance gains for FinFET-based standard cells. The experimental results on 12 distinct complex cells validate the aging mitigation across years.

The heightened sensitivity of circuit behavior to manufacturing processes is surging in sub-nanometer nodes, presenting a formidable challenge for high-dimensional performance modeling. Estimating circuit performance variations resulting from process randomness in upcoming technology nodes aids in implementing appropriate countermeasures to handle them. Addressing this, we introduce a multi-node transfer learning method for predicting the performance of VLSI digital circuits. The proposed method facilitates Zero-shot Learning in future technology nodes based on insights gained from analyzing the circuit’s process-induced behavior in established nodes. The approach is fast, scalable, and robust, leveraging digital standard cell characterization that can be applied to estimate the performance of a wide array of intricate circuits. Experimental results affirm the approach’s exceptional data efficiency, achieving a speed increase of up to 104times, all the while maintaining superior accuracy compared to traditional simulators.

Analog circuit characterization is a pivotal phase in the design process, serving to validate their performance. It ensures that design choices lead to optimization, ultimately resulting in reliable and robust designs. The conventional, resource-intensive circuit characterization using traditional simulation tools is being supplanted by Machine Learning surrogate models, providing computational advantages. Nevertheless, these models tend to be specific to particular circuits, demanding significant design efforts and often lacking reusability for other designs and topologies. In this paper, we introduce a Unified Neural Network Architecture that is versatile and computationally efficient. It offers a streamlined and effective approach for modeling a diverse array of analog circuits prone to process, voltage, and temperature variations. Experimental trials conducted on various circuits in CMOS 180nm, 65nm and 28nm technologies demonstrate a mean average percentage error of less than 1%, affirming the effectiveness and reusability of the proposed architecture. This leads to substantial savings in design time, computational resources, and characterization costs.

Modeling process, operating condition, reliability, and technological variation in transistor level design introduce significant variance in device and performance parameters. This paper presents a machine-learning-based architectural approach that enhances model performance, as a step towards developing Foundation Models tailored for circuit data, accurately capturing technology and process-induced variations in leakage power, voltage, and current. The architecture incorporates the impact of varying operating conditions, including temperatures from -55°C to 125°C and supply voltage fluctuations of ±10% on 16nm HP FinFET, and 16nm, 22nm, 32nm, and 45nm HP-MGK CMOS technology nodes. By enhancing the performance of baseline machine-learning models using a chained architecture to cater to the high variance in target, our C-Arch serves as a versatile framework for circuit modeling when the data spread is high. Experimental results on current, voltage, and leakage power estimation, demonstrate average improvements of up to 93.76%, 96.17%, and 88.82% in Mean Absolute Percentage Error compared to baseline models, underscoring the computational savings and viability of Foundation Models in circuit design.

This paper presents a dual-loop reference-less Clock and Data Recovery (CDR) architecture implemented in TSMC 28nm CMOS process for PCIe Gen4 application. A novel frequency detector (FD) with a wide frequency acquisition range is presented, which avoids harmonic locking issues and is independent of input transition density. The FD demonstrates rapid locking capabilities, securing synchronization with Pseudo-Random Binary Sequences (PRBS7 and PRBS31) data patterns. The locking time from PCIe Gen1 (2.5Gbps) to PCIe Gen4 (16Gbps) is less than 5us. The CDR achieves a power consumption of less than 2mW with a supply voltage of 0.9V.

This paper explores advanced voltage reference designs with extremely low power consumption, specifically under one microwatt. These designs are crucial for portable electronic systems that rely on minimal power. The paper categorizes these voltage references based on the types of devices used to generate temperature-proportional or complementary signals. It also provides key design examples to illustrate these concepts. Additionally, the paper enhances understanding by conducting a comparative analysis of performance metrics, including power consumption, temperature coefficient, physical size, and resistance to variations in the manufacturing process.

In this paper, a cost-effective and easy-to-implement auto-trim technique is introduced for PTAT or CTAT resistance based current references. The resistance with a process-insensitive temperature coefficient requires only a single point trim at room temperature, achieving process and temperature-insensitive current. The approach utilizes a data comparison method where on-chip current data is compared with off-chip reference data to trim the resistance of the current reference. The off-chip reference data is generated using a low-cost external resistor that is used only during the trimming operation. The proposed trimming sensor effectively calibrates the current, offering precision close to manual trimming, leading to cost, time, and resource savings. To validate the working of the proposed technique, the auto trim sensor with on-chip current reference is designed in TSMC 180nm technology. The auto trim sensor calibrates the on-chip current from ±25% (due to voltage and resistance process variation) to ±1.5% across the process and 3σ mismatch.

Aggressive scaling down of transistor dimensions has made process-aware circuit modeling a crucial task. Achieving accurate circuit modeling requires lengthy and resource-intensive simulations. Machine Learning-based surrogate models, offering computational efficiency and speed, are viable alternatives to traditional simulators. This paper introduces a meta-learning approach designed to accurately capture process-induced variations in the leakage power of VLSI circuits. The impact of a wide range of fluctuations in operating conditions, including temperature (-55°C to 125°C) and supply voltage (±10%) has also been incorporated for leakage modeling. The proposed meta-learning model is versatile, enhancing the performance of underlying baseline machine-learning models while eliminating the need for time-consuming hyperparameter optimization. Our experiments on leakage estimation using 16 and 7 nanometer FinFET technology nodes demonstrate an average improvement of up to 50% and 48% in Mean Absolute Percentage Error compared to stand-alone baseline models.

Biomolecules play indispensable roles in all life processes, including disease development, so their accurate detection is critical to cell investigation, medical diagnosis, and treatment. Radio Frequency (RF) sensing has become a popular testing technique compared to traditional methods for biomolecule analysis, providing results in less time using less volume of samples. It allows rapid, sensitive, real-time measurements and label-free techniques. However, for taking the signals from biomolecules over the RF-based sensors we need to connect them with the Vector Network Analyzer (VNA) which is a bulky and costly device. To develop an RF sensing based a true lab-on-chip (LoC) device, the paper presents a fully integrated low-power less-area on-chip single port CMOS VNA designed in 65nm CMOS technology to detect the bio-molecule The proposed design works in a tunable frequency range of 0.5 GHz to 2.5 GHz, ensuring much higher precision. Using an RLC equivalent of an Interdigitated capacitor (IDC) sensor, a fully integrated architecture for detecting S11of the biomolecules has been introduced. A resistive bridge coupler is used for wideband operation with a directivity of 14.89dB up to 2.5 GHz. Also, a high-linearity LNA (low noise amplifier) is designed with a linearity of -13dB and a gain of 14dB. The LNA has a low NF of 3.21dB. The design occupies an active area of 0.01767mm2and consumes power of 41mW from a 1 V supply.

1Generative AI has seen remarkable growth over the past few years, with diffusion models being state-of-the-art for image generation. This study investigates the use of diffusion models in artificial data generation for electronic circuits to enhance the accuracy of subsequent machine learning models in tasks such as performance assessment, design, and testing when training data is usually known to be very limited. We utilize simulations in the HSPICE design environment with 22nm CMOS technology nodes to obtain representative real training data for our proposed diffusion model. Our results demonstrate the close resemblance of synthetic data using diffusion models to real data. We validate the quality of generated data and demonstrate that data augmentation is certainly effective in the predictive analysis of VLSI design for digital circuits.

Designing robust performance models for modern complex digital circuits in the face of rapidly accelerating process variations is a critical yet demanding task. This paper introduces an efficient statistical performance modeling approach for VLSI digital circuits that incurs minimal computational expense. The fundamental concept involves capitalizing on knowledge gained from circuit modeling in one technology node to streamline the modeling process in another. This is achieved by merging previously established statistical models of process technology with a limited set of simulation data from a subsequent process technology through transfer learning. Comprehensive experiments conducted across diverse technology nodes demonstrate that the proposed framework is robust, precise, efficient in data usage, and computationally superior to other cutting-edge performance modeling techniques.

This paper proposes a switched-capacitor network (SCN) based fractional bandgap voltage reference (BGR) circuit designed in 180nm CMOS process to achieve high accuracy and low power consumption for implantable biomedical applications. The design proposes a$V_{EB}$generator that employs a 2x charge pump and an improved SCN to generate a temperature inde-pendent reference voltage$(V_{REF})$’. A low-power clock generator circuit is proposed, which reduces the leakage current by 37 % compared to previous works, thereby reducing the circuit's power consumption to 18.5nW at typical conditions. The design works from a supply voltage of 0.5V and has a TC of 74. Sppm/${}^{\circ} \mathrm{C}$over a temperature range of$0-80^{\circ} \mathrm{C}$. The PSRR of the circuit is -62.9dB at 100Hz. Based on the Monte Carlo simulations of 500 samples, we obtain an untrimmed$3\sigma/\mu$of 2.6%. The design occupies an active area of 0.027mm2.

This paper proposes a 162nW resistance-to-digital converter (RDC) for miniature battery-powered sensing systems. The RDC first converts input resistance to a pulse by charging a capacitor to a threshold voltage with a current proportional to the resistance. It compensates temperature sensitivity of the charging current by generating the threshold voltage with the same temperature dependency. Then, the circuit digitizes the pulse using an up-down counter that cancels temperature-dependent delay and offset of the low-power comparator in a digital Correlated Double Sampling (CDS) style. Designed in a 180 nm CMOS process, the proposed circuit achieves a figure-of-merit (FoM) of 0.845pJ/c.s. in simulation, with a conversion time of 50 ms for input resistance from$50\mathrm{k}\Omega$to$1\mathrm{M}\Omega$, while consuming 162nW at a supply voltage of 900 mV. Also, it obtains a temperature sensitivity of 26.9ppm/°C from −40 to 100°C. Compared with the state-of-the-art RDCs, this work improves the FoM and temperature sensitivity by 42.91% and 11.52%, respectively.

The paper presents a novel nW range gate-leakage-based Sub-Bandgap Voltage Reference (sub-BGR) for low-power and high-temperature range IoT applications. It generates a reference voltage of 336mV without incorporating any resistors and operating for a high-temperature range of −40°C to 150°C and a supply range of 0.7V-4V. In the above temperature and supply ranges, the proposed circuit's power consumption only goes up by 30x and 1.025x times, respectively. Designed in a 65nm CMOS process, the proposed architecture achieves an accuracy of 94ppm/°C. It achieves a line sensitivity of 0.0066%/V for a supply range of 0.7V to 4V and a PSRR of 89dB at DC and 1V supply. The proposed circuit shows$\mathrm{a}\pm 3\sigma$-inaccuracy of 4.295% without additional trimming circuits. It occupies only 0.0851mm2 area while consuming only 2.3nW at 27°C and 21.74nW at 150°C for a 0.7V supply.

This paper proposes a low-power relaxation oscillator for low-power high-temperature IoT systems. It generates a 959 Hz clock signal from −40 to 170°C, consuming 6.75 nW at 0.65 V. A proposed switch-leakage compensation scheme nullifies the effects of body diode and subthreshold leakages on oscillator output frequency at high temperatures, thereby obtaining a wide operating temperature range. The oscillator implemented in a 180 nm CMOS process achieves a temperature coefficient of 40 ppm/°C from −40 to 170 °C at 0.65 V and a line sensitivity of 0.5 %/V from 0.65 to 2.4 V at room temperature, in simulation. Compared with state-of-the-art sub-$\mu\mathrm{W}$oscillators, this circuit obtains the highest operating temperature and the maximum temperature range.

The paper presents a 0.5V supply, gate leakage-based current/voltage reference for ultra-low power IoT and biomedical applications. The references are generated by the proper addition of PTAT and CTAT curves, which are obtained by exploiting the traditional architecture of the beta multiplier and using the body biasing effect. Gate leakage transistors replace the resistors to ensure low power and low area. The circuit doesn't involve any Op-Amps avoiding the issues of offset that are prominent in these circuits. Implemented in CMOS 90nm technology, the proposed current (voltage) reference achieves a typical accuracy of$34.6\text{ppm} /{ }^{\circ} \mathrm{C}(29.68 \text{ppm} /{ }^{\circ} \mathrm{C})$over a wide temperature range of$-55^{\circ} \mathrm{C}$to$75^{\circ}\mathrm{C}$with typical value 63.32pA(0.35V). Excellent line sensitivity of 0.0318%N and 0.0576%N are observed for voltage and current reference, respectively, in a supply range of 0.5V - 2.3V. The area occupied by the total circuit is 0.0096mm2, while the power consumption is 415pW at the typical corner of$27^{\circ}C$and 0.5V supply.

The paper presents a sub-nW gate-leakage based voltage and current reference in a single circuit whose reference values are scalable and doesn’t incorporate start-up circuits or resistors in the architecture. The power consumption of the proposed circuit increases by only 2.1x in the temperature range of -55°C to 100°C, unlike conventional voltage/current references where the power consumption increases exponentially w.r.t temperature. Implemented in 90nm technology, the proposed voltage reference (current reference) achieves post-trim typical accuracy of 22ppm/°C(58ppm/°C) and worst-case accuracy of 71ppm/°C(78ppm/°C). Excellent line sensitivities of 0.029%/V and 0.059%/V are observed for voltage and current reference respectively, in a supply range of 1V - 3V. Without any start-up circuit, the observed 99% settling times for voltage and current reference are 1.92ms and 2.526ms respectively. The area occupied by the total circuit is 0.0015mm2, while the power consumption is 156pW at typical corner, 27°C and 1V supply.

In this paper, we aim at optimizing the leakage power and propagation delays using optimization algorithms like Glowworm Swarm Optimization and Neighbourhood Cultivation Genetic Algorithm, subjected to variations in Process, Voltage, Temperature and Aging degradation (PVTA) targeting low power or high performance applications. For high performance applications, we synthesize transistor sizes, at which the critical path delay in worst case PVT conditions with 3 years of NBTI aging degradation is optimized below the critical path delay (of initial sizing) at nominal conditions keeping power budget in bound. On the other hand, for low power applications, we obtain transistor sizing where leakage is reduced by more than 50%, keeping a bound on critical path delay. All the pre and post stress simulations are performed using HSPICE for 22nm Metal Gate High-K dielectric model parameters. The temperature range and the supply voltage ranges are -55 °C to 125 °C and 0.72V to 0.88V respectively. The process parameters are considered at ±3σ variation. The ingenuous working of the circuits for the obtained sizing is ensured by Monte Carlo analysis evaluating over entire range of process variations and operating conditions for intended life time.

The paper presents a sub-nW BJT based temperature sensor for ultra-low power microsystems. The sensor is based on amplifying the difference between base-emitter voltages of BJTs using gate-leakage transistors. Implemented in UMC 65nm technology, the sensor occupies an area of 0.005mm2. It achieves a maximum non-linearity error of 0.12oC(3σ) over the temperature range of - 55oC to 80oC. Without any trimming, a worst case inaccuracy of +0.36oC/ - 1.61oC is observed w.r.t process variations, depicting the process-invariant nature of the temperature sensor. It also achieves a low supply sensitivity of 0.56oC/V over a wide supply range of 0.7V-3V. The power consumption of the sensor is 419pW at 27oC and 0.7V supply.

This paper proposes low power optimization using a modified swarm intelligence algorithm for the scenarios-transistor sizing based static power reduction and low power standard cell generation for approximate computing. In these scenarios, we explore a lower abstraction level and see how standard cells can be tuned to a power-delay-quality optimal point. For transistor sizing, the algorithm considers fabrication process parameter variations (for ±3σ design) in addition to a wide range of temperature (-55°C to 125°C) and supply voltage (±10%) variations to yield PVT aware robust sizing solutions. The approach has been applied on numerous single and multistage circuits (including ISCAS benchmarks) while proposing a dual sizing solution for non-critical and critical path cells. For approximate systems, we present algorithm-generated full adder designs for speech processing systems. The designs vary in terms of accuracy and power. Results show leakage reductions up to 58.2% for conventional and 66.8% with approximation designs for 22nm metal gate high-K (MGK) technology cells.

Approximate Computing has paved way for elaborate savings in design area and latency of modern system architectures processing images or signals, by a deliberate yet tolerable loss of functional accuracy. This paper thus proposes a design of an approximate multiplier based on the efficient Toom-Cook algorithm, that has a lower complexity of O(Nlogd(zd-1)) than O(N2), for order d. Inherent integer divisions in the algorithm has restricted its feasibility in hardware, unless without suitable approximation. On an average, the proposed multiplier achieves 53%, 18% and 57% improvements in area, delay and power only with less than 1% mean error. Owing to these benefits due to lower computational complexity, the multiplier can be configured to achieve significant savings with a high quality output and that suits well to the nature of the speech processing systems, hence the design works well for the epoch extraction system in speech.

In this paper, we propose an accurate and computationally efficient Gradient Boosting approach for the estimation of statistical variations aware leakage power and propagation delay in the CMOS/FinFET standard digital cells. The proposed model estimates the leakage power and propagation delay w.r.t variations in process, temperature (-55°C to 125°C) and supply voltage(±10% variations). The distinguishing feature of the proposed approach is its compatibility with both CMOS and FinFET technologies. Moreover, the performance of the proposed model is consistent with various technology nodes. Exhaustive tests report an average error of <; 1% in 16nm CMOS and FinFET standard digital cells w.r.t analog HSPICE simulations with several orders increase in computational speed. Further, the complex cell estimation can be carried out through precharacterized standard cells abstaining longer simulations.

This work presents a highly accurate current reference of 66nA for ultra-low power applications. A very low figure-of-merit (FOM) of 1.3501ppm/°C2is achieved on consuming a minimal quiescent current of 199.37nA. To cancel out process variations, the current subtraction technique is employed and a β-multiplier is used to compensate for mobility (μ) and threshold voltage (Vth). In addition, curvature compensation technique backed by PTAT and CTAT current cancellation is adopted to attain a lower temperature coefficient (TC). Hence, an imperceptible variation of accuracy with temperature and supply variations across all process corners is attained. An adopted trimming scheme further minimizes the overall process spread to ±1.515% without compromising on accuracy. Accordingly, a TC of 67.04ppm/°C over a wide temperature range of -50° C to 100° C is obtained. Furthermore, 1.413%/V line sensitivity (LS) in the supply range of 1.38V to 3V is observed. Low power consumption of [email protected] facilitates its use in high-performance low power applications.

This brief presents a novel low-power and area-efficient LDO that satisfies all primary requirements of power mapping for a Power Management IC (PMIC). The design introduces a dynamically biased feedback resistor which responds instantly to the output voltage variations, thereby achieving better load transient behavior. Besides, the employed adaptive-biasing technique contributes in architectural transformation to attain stability over a wider range of load currents (0-100mA). It provides a regulated voltage of 1.87V from a supply ranging from 1.92V to 3.6V with a reported load and line regulation of 0.00136mV/mA and 0.078mV/V respectively. Moreover, the circuit potentially supports the load transients either from 0A to 100mA or 100mA to 0A with a rise and fall times of 10 μs. The achieved overshoot and undershoot values are 160mV and 154mV respectively. Hence, it demonstrates a substantial steady-state and transient performance with low-power thus making it suitable for battery-operated portable devices.

Higher yield, climbing transistor counts and shrinking dimensions of a single Integrated Circuit (IC) have always been the demands in the fabrication market. However, this increased complexity and miniaturization of transistors present a challenge, due to high critical process variations combined with a ubiquitous presence of temperature and supply voltage variations, to achieve the required specification bounds on the desired performance of the circuits. Since optimization has become a very crucial task in IC design, the paper presents an efficient transistor sizing based optimization technique of the CMOS circuits to achieve low power, high performance and high yield design goals. The proposed memetic algorithm judiciously utilizes a threshold based local search procedure to improve convergence in its inherent genetic nature. The algorithm optimizes with the effect of temperature [-55 to 125]°C and supply voltage ±10% variations and in addition a number of statistically sampled sets generated as Gaussian, Latin Hypercube and Correlation Screened schemes of process variations. The proposed technique is applicable to any technology node and has been tested over several standard single-stage and some complex multi-stage digital circuits designed using a Multi-Gate high-K dielectric (MGK) 22nm CMOS model. The reduction in leakage power with propagation delay goes as high as 53% with 9% respectively, as observed across the various digital circuits.

Enormous increase in process variations (due to progressive CMOS technology scaling) along-with the temperature and supply voltage variations are severely degrading the fabrication outcome of digital circuits i.e. circuits are not accomplishing the specification bounds of the required performances. Therefore, process and operating variations aware optimization has become a very essential task in VLSI design. Moreover, many specifications in a circuit have challenging trade-offs, hence demand effective optimization skills. With this vision, this paper presents optimization algorithm based robust transistor sizing for various nanoscale CMOS digital circuits. The objective is to minimize the static i.e. leakage power without degrading the operating frequency (i.e. keeping the propagation delays in bound) and area. The reported results are shown for 32nm CMOS Metal gate High-k model parameters, however methodology is equally valid for further scaled technology nodes. The Overall reduction in leakage power obtained is up to 88% keeping bound on the critical path delay. The temperature range and supply voltage has been taken between -55°C to +125°C (for automotive applications) and 0.90V to 1.10V (±10% variations) respectively at 3-sigma design.

In this paper, a highly stable all CMOS current reference against temperature and supply variation is proposed. Current reference of 5μA and 50nA has been designed for low power and ultra-low power applications respectively. The reference architecture is based on ratio between the PTAT voltage and the PTAT resistance. The process insensitive temperature compensation is accomplished by dividing TC of voltage with process insensitive TC of resistor. A high PSRR, process independent voltage reference is designed for PTAT voltage. N-poly on chip resistor is used for PTAT resistance. The proposed current reference is implemented in 0.18-μm TSMC technology. The architecture achieved PSRR of 74dB and line sensitivity of 0.05% works at supply voltage variation from 1.4V to 3.6V. The current reference of 5μA and 50nA attain temperature coefficient of 11.6 ppm/°C and 12.2 ppm/°C respectively for the temperature variation of -55° C to 125° C.

A sub 1-V, ultra low power temperature sensor has been implemented in TSMC 180 nm. The architecture is digital friendly since it creates a pulse width modulated wave instead of voltage. It uses proportional to absolute temperature(PTAT) characteristics of resistance to generate PTAT delay. Temperature to delay conversion depends only on passive elements, thereby making the circuit insensitive to supply variations. Line sensitivity of 0.23 °C/V is achieved for a wide supply range of 0.7-3.6V. A non linearity error of less than 0.8 °C is measured for -55 to 125 °C using linear fit curve. This occupies an area of 0.82 mm2and consumes a power of 47 nW at 0.8 V supply.

Logic-level estimators of leakage currents, in nanoscale standard-cell-based designs, are relevant for the dramatic speed advantage with respect to analog SPICE-level simulation. We propose a novel logic-level leakage estimation model based on the characterization of voltages at the internal nodes of digital cells, in conjunction with the characterization of leakage currents in a single field-effect transistor (FET) device and with the input-dependent Kirchhoff current law expression of the total current in the cell topology. The voltage-based nature of the approach simplifies the inclusion of supply voltage variation/scaling impact, as well as of output voltage drop (loading effect), on leakage currents. The method has been implemented in hardware description language models of a complete cell library. Exhaustive tests report average accuracy below 1% error in 22-nm CMOS and 20-nm FinFET technologies, when compared with SPICE BSIM simulation results.