Nowadays, electronic portable systems with excellent battery life are the new standard. Hence, today’s circuits must be designed targeting low power consumption. Moreover, a key component of any system is its voltage reference, which must be accurate and reliable while presenting low power consumption. This work presents a novel low-power 0.5V voltage reference, designed in a 65nm CMOS technology using an integrated differential pair trimming technique, able to achieve 28.1ppm/°C of temperature coefficient.

Integrated circuit design in millimeter-wave (mm-Wave) bands is exceptionally complex and dependent on costly electromagnetic (EM) simulations. Therefore, in the past few years, a growing interest has emerged in developing novel optimization-based methodologies for the automatic design of mm-Wave circuits. However, current approaches lack scalability when the circuit/system complexity increases. Besides, many also depend on EM simulators, which degrade their efficiency. This work resorts to hierarchical system partitioning and bottom-up design approaches, where a precise machine learning model - composed of hundreds of seamlessly integrated sub-models that guarantee high accuracy (validated against EM simulations and measurements) up to 200GHz - is embedded to design passive components, e.g., transformers and baluns. The model generates optimal design surfaces to be fed to the hierarchical levels above or acts as a performance estimator. With the proposed scheme, it is possible to remove the dependency of EM simulations during optimization. The proposed mixed-optimal-surface, performance estimator, and simulation-based bottom-up multiobjective optimization (MOO) are used to fully design a Ka-band mm-Wave transmitter from the device up to the system level in 65-nm CMOS for state-of-the-art specifications.

Low-noise amplifiers (LNAs) play a significant role in modern millimeter-wave (mmWave) integrated circuit multi-standard transceiver systems. This paper proposes a transformer-less LNA based on a cascade of two AC coupled common source stages, each with inductive degeneration, for the 28-GHz 5G communications band. An automatic design methodology explores the topology design space over a 148-dimensional performance space spreading through different corners for process, voltage, and temperature. It results in about 1000 optimized LNA variants, with gains achieving up to 17.5-dB, and the noise Figure and power consumption down to 2.4-dB and 1.86-mW, respectively. These performance figures position the adopted LNA’s performance boundaries with the most recent mmWave LNAs.

This work presents a 16MHz on_chip oscillator based on a frequency_locked loop (FLL) which is radiation hardened by design and intended for space applications. The oscillator maintains a high temperature stability over a wide range oftemperatures, process and voltage variations. The FLL presents a 7-stage current starved ring oscillator using output signal combiners for lower radiation sensitivity. Furthermore, the system presents high reconfigurability by using several DACs to adjust the oscillator’s output frequency and the temperature slope dependency. This reconfigurability is especially useful to maintain all performances across PVT variations.

This paper presents an innovative approach toward the automation of the placement task of analog integrated circuit layout design by using an artificial neural network that generates multiple valid floorplan solutions at push-button speed. The proposed model extends the knowledge mining of the most recent layout generation techniques as an end-to-end approach. A novel loss function is used in a semi-supervised fashion for the model to learn how to generate effective placements that follow topological constraints instead of simply trying to copy devices' locations from some pre-existing labeled dataset of placement solutions. Thus, in addition to eliminating the need for a large dataset of placed sizing solutions for training, the model generalizes better and outputs better layouts for solutions outside the training set. Moreover, one step further is taken towards a model that can predict the placement of different circuit topologies by supporting different encodings (with different number of devices) on the input layer of the artificial neural networks, ultimately fostering an opportunity to reuse incomplete legacy layout information. As proof of concept, the one ANN trained for 2 amplifier topologies is used to produce multiple placement recommendations in less than 40 ms for new designs.

A new strategy for millimeter-wave (mm-Wave) circuit and system synthesis, where the accuracy of electromagnetic simulations can be achieved in optimization-based design methodologies without sacrificing efficiency, is presented and tested within a real industrial project. This is done by properly partitioning the system, generating libraries of passive devices which are electromagnetically simulated prior to any circuit optimization, generating performance tradeoffs at different hierarchical levels with multiobjective optimization algorithms and hierarchically composing lower level sub-blocks. With this proposed solution, an entire mm-Wave system, from the passive component level up to the system level, has been designed and compared with the results obtained from a conventional design approach, demonstrating the outstanding capabilities of the methodology.

In recent years there has been a growing interest in electronic design automation methodologies for the optimization-based design of radio frequency (RF) circuits and systems. While for simple circuits several successful methodologies have been proposed, these very same methodologies exhibit significant deficiencies when the complexity of the circuit is increased. The majority of the published methodologies that can tackle RF systems are either based on high-level system specification tools or use models to estimate the system performances. Hence, such approaches do not usually provide the desired accuracy for RF systems. In this paper, a methodology based on hierarchical multilevel bottom-up design approaches is presented, where multiobjective optimization algorithms are used to design an entire RF system from the passive component level up to the system level. Furthermore, each level of the hierarchy is simulated with the highest accuracy possible: electromagnetic simulation accuracy at device-level and electrical simulations at circuit/system-level.

Process variability and time-dependent variability have become major concerns in deeply-scaled technologies. Two of the most important time-dependent variability phenomena are Bias Temperature Instability (BTI) and Hot-Carrier Injection (HCI), which can critically shorten the lifetime of circuits. Both BTI and HCI reveal a discrete and stochastic behavior in the nanometer scale, and, while process variability has been extensively treated, there is a lack of design methodologies that address the joint impact of these two phenomena on circuits. In this work, an automated and time-efficient design methodology that takes into account both process and time-dependent variability is presented. This methodology is based on the utilization of lifetime-aware Pareto-Optimal Fronts (POFs). The POFs are generated with a multi-objective optimization algorithm linked to a stochastic simulator. Both the optimization algorithm and the simulator have been specifically tailored to reduce the computational cost of the accurate evaluation of the impact on a circuit of both sources of variability.

In this paper, an analysis of the methodologies proposed in the past years to automate the synthesis of radio-frequency (RF) integrated circuit blocks is presented. In the light of this analysis, and to avoid nonsystematic iterations between sizing and layout design steps, a multiobjective optimization-based layout-aware sizing approach with preoptimized integrated inductor(s) design space is proposed. An automatic layout generation from netlist to ready-to-fabricate prototype is carried in-the-loop for each tentative sizing solution using an RF-specific module generator, template-based placer and evolutionary multinet router with preoptimized interconnect widths. The proposed approach exploits the full capabilities of the most established computer-aided design tools for RF design available nowadays, i.e., RF circuit simulator as performance evaluator, electromagnetic simulator for inductor characterization, and layout extractor to determine the complete circuit layout parasitics. Experiments are conducted over a widely used circuit in the RF context, showing the advantages of performing complete layout-aware sizing optimization from the very initial stages of the design process.

This paper describes a class of real-life optimization problems that has not been addressed before: a multi-objective optimization in which one objective is neither minimized nor maximized but uniformly swept over a wide range. The limitations of conventional multi-objective optimization algorithms to deal with this kind of problems are illustrated via the optimization of radiofrequency inductors. For the first time, an algorithm is proposed that provides sets of solutions for this kind of problems.

The accuracy of high-frequency models of passive RF devices, e.g., inductors or transformers, presents one of the most challenging problems for RF integrated circuits. Accuracy limitations lead RF designers to time-consuming iterations with electromagnetic simulators. This paper will explore and compare two advanced modeling techniques. The first one is based on the segmented model approach, in which each device segment is characterized with a lumped element model. The second technique is based on the generation of surrogate models from the electromagnetic simulation of a set of device samples. Different modeling strategies (frequency separation, filtering according to self-resonance frequency, etc.) will be considered. Efficiency and accuracy of both, physical and surrogate, modeling techniques will be compared using a Si process technology.