Chips

Artificial intelligence is redefining the computing landscape. Chiplets and heterogeneous integration have become the key strategies for current and next-generation processors. In the wake of Moore’s law slowing down, system integration through advanced packaging has emerged as the leading approach to achieve the highest performance per cost. Overall, the system is converging around substrate which is the main component of packaging. Glass stands out as the superior integration platform for chiplet-based systems. Glass substrates provide unmatched electrical and mechanical properties leading to unprecedented design and integration flexibility at a lower cost than competitive technologies. Three key advantages make glass the platform of choice: the ability to tune material properties, the ability to structure glass, and the feasibility of processing on a large panel scale. This review details the fundamentals of glass processing and manufacturing, innovative integration techniques, and cutting-edge research that collectively position glass substrate as a superior option for the next-generation systems for AI and beyond. Finally, we outline how technology must be shaped in the coming years to drive system scaling. Full article

Bias Temperature Instability (BTI)-induced aging of transistors is a serious concern in modern electronic circuits, yet its effects on the operation of mixed-signal circuits have not been extensively studied. In this work, initially we analyze how BTI-induced aging degradation influences the analog front end of Flash analog-to-digital converters (ADCs). BTI-induced aging leads to substantial increments in the offset voltage of the ADC comparators, which in turn affect their trip point voltage, leading to the alteration of the ADC’s performance characteristics, such as gain, full-scale error and integral nonlinearity. Thus, erroneous responses are generated. Next, we propose a low-cost BTI-induced aging mitigation technique based on a circuit reconfiguration method which periodically alters the average voltage stress on the ADC comparators’ transistors. The proposed method limits the comparators’ offset voltage development, restricting the shift in their trip point voltage. Consequently, the impact of aging on the performance characteristics of the ADC is drastically reduced, and its reliability is improved. According to our simulations, after two years of operation, the gain error is reduced by 95.43%, the full-scale error is reduced by 63.31% and the integral nonlinearity is reduced by 63.00%, with respect to operation without applying the proposed aging mitigation technique. Full article

In recent years, many electronic device industries have shown interest in using artificial intelligence (AI) to quickly estimate package warpage. Machine learning is one of the AI techniques which will give an express prediction on package warpage with the help of several attributes of the data and different algorithms. This study uses a deep learning (DL) model which combines with a deep neural network (DNN) technique and finite element analysis (FEA) to estimate the package warpage of a mobile universal flash storage (UFS) package. Developing a DL model requires a training database from finite element simulation results and a DNN algorithm. The developed DL model accuracy for package warpage is calculated by validating FEA simulation results and experiment data. The error between the DL model prediction and FEA simulation result is less than 7%. This proposed approach can help effectively and efficiently assess package warpage for new product introduction (NPI) with less FEA simulation work and less test vehicle of a real package for warpage measurement and assessment. Full article

Drawing inspiration from biological brains’ energy-efficient information-processing mechanisms, photonic integrated circuits (PICs) have facilitated the development of ultrafast artificial neural networks. This in turn is envisaged to offer potential solutions to the growing demand for artificial intelligence employing machine learning in various domains, from nonlinear optimization and telecommunication to medical diagnosis. In the meantime, silicon photonics has emerged as a mainstream technology for integrated chip-based applications. However, challenges still need to be addressed in scaling it further for broader applications due to the requirement of co-integration of electronic circuitry for control and calibration. Leveraging physics in algorithms and nanoscale materials holds promise for achieving low-power miniaturized chips capable of real-time inference and learning. Against this backdrop, we present the State of the Art in neuromorphic photonic computing, focusing primarily on architecture, weighting mechanisms, photonic neurons, and training, while giving an overall view of recent advancements, challenges, and prospects. We also emphasize and highlight the need for revolutionary hardware innovations to scale up neuromorphic systems while enhancing energy efficiency and performance. Full article

Nowadays, Graphical Processor Units (GPUs) are a great technology to implement Artificial Intelligence (AI) processes; however, a challenge arises when the inclusion of a GPU is not feasible due to the cost, power consumption, or the size of the hardware. This issue is particularly relevant for portable devices, such as laptops or smartphones, where the inclusion of a dedicated GPU is not the best option. One possible solution to that problem is the use of a CPU with AI capabilities, i.e., parallelism and high performance. In particular, RISC-V architecture is considered a good open-source candidate to support such tasks. These capabilities are based on vector operations that, by definition, operate over many elements at the same time, allowing for the execution of SIMD instructions that can be used to implement typical AI routines and procedures. In this context, the main purpose of this proposal is to develop an ASIC Vector Engine RISC-V architecture compliant that implements a minimum set of the Vector Extension capable of the parallel processing of multiple data elements with a single instruction. These instructions operate on vectors and involve addition, multiplication, logical, comparison, and permutation operations. Especially, the multiplication was implemented using the Vedic multiplication algorithm. Contributions include the description of the design, synthesis, and validation processes to develop the ASIC, and a performance comparison between the FPGA implementation and the ASIC using different nanometric technologies, where the best performance of 110 MHz, and the best implementation in terms of silicon area, was achieved by 7 nm technology. Full article

The fast-maturing photonic integration technology is calling for a versatile platform that supports both active and passive functions as well as high scalability through component miniaturization. Indium phosphide (InP) has long been recognized for its ability to deliver a comprehensive suite of photonic components. InP membrane technology has emerged as a next-generation solution that could unite the functional completeness with high scalability. This paper describes recent advancements in the InP-membrane-on-Si (IMOS) platform, which supports high-performance passives, polarization and mode handling, native light sources, amplifiers, modulators and detectors, and novel material integration. Full article

This paper deals with a standard cell-based analog-in-concept pW-power building block as a comparator and a wake-up oscillator. Both topologies, traditionally conceived as an analog building block made by a custom process and supply voltage-dependent design flow, are designed only by using digital gates, enabling them to be automated and fully synthesizable. This further results in supply voltage scalability and regulator-less operation, allowing direct powering by an energy harvester without additional ancillary circuit blocks (such as current and voltage sources). In particular, the circuit similarities in implementing a rail-to-rail dynamic voltage comparator and a relaxation oscillator using only digital gates are discussed. The building blocks previously reported in the literature by the author will be described, and the common root of their design will be highlighted. Full article

A multifunctional circuit with high-pass and low-pass negative group delays can be achieved by simply changing the values of the components in the circuit, without changing the circuit structure. While achieving negative group delay, the circuit also has flat broadband characteristics and lower losses. Theoretical calculations and equation derivation are provided. The experimental circuit was simulated, and the simulation results were essentially consistent with the theoretical results, indicating the feasibility of the experiment. Full article

A new control system implemented with a single-stage DC-DC controller to power an LED headlamp for automotive applications is presented in this work. Daytime running light (DRL), low beam (LB), high beam (HB) and adaptive driving beam (ADB) are typical functions requiring a dedicated LED driver solution to fulfill car maker requirements for front-light applications. Single-stage drivers often exhibit a significant overshoot in LED current during transitions from driving a higher number of LEDs to a lower number. To maintain LED reliability, this current overshoot must remain below the maximum current rating of the LEDs. If the overshoot overcomes this limit, it can cause permanent damage to the LEDs or reduce their lifespan. To preserve LED reliability, a comprehensive system has been proposed to minimize the peak of LED current overshoots, especially during transitions between different operating modes or LED string configurations. A key feature of the proposed system is the implementation of a parallel discharging path to be activated only when the current flowing in the LEDs is higher than a predefined threshold. A prototype incorporating an integrated test chip has been developed to validate this approach. Measurement results and comparison with state-of-the-art solutions available in the market are shown. Furthermore, a critical aspect to be considered is the proper dimensioning of the discharging path. It requires careful considerations about the gate driver capabilities, the discharging resistor values, and the thermal management of the dumping element. For this purpose, an extensive study on how to size the relative components is also presented. Full article

On-chip optical accelerometers can be a promising alternative to capacitive, piezo-resistive, and piezo-electric accelerometers in some applications due to their immunity to electromagnetic interference and high sensitivity, which allow for robust operation in electromagnetically noisy environments. This paper focuses on the characterization of an easy-to-fabricate tri-axial fiber-free optical MEMS accelerometer, which employs a simple assembly consisting of a light emitting diode (LED), a quadrant photodetector (QPD), and a suspended proof mass, measuring acceleration through light power modulation. This configuration enables simple readout circuitry without the need for complex digital signal processing (DSP). Performance modeling was conducted to simulate the LED’s irradiance profile and its interaction with the proof mass and QPD. Additionally, experimental tests were performed to measure the device’s mechanical sensitivity and validate the mechanical model. Lateral mechanical sensitivity is obtained with acceptable discrepancy from that obtained from FEA simulations. This work consolidates the performance of the design adapted and demonstrates the accelerometer’s feasibility for practical applications. Full article

Fully differential amplifier circuits are well suited for instrumentation front ends and signal-conditioning applications. They offer high common-mode rejection ratios (CMRRs) regardless of the passive component tolerances but remain sensitive to imbalances in active devices. By using power supply bootstrapping (PSB), the CMRRs of these circuits can be improved, where they become independent of mismatches in both passive and active components. This technique works by forcing the power supply nodes to follow the common-mode input voltage, which significantly enhances the CMRR. However, this approach introduces stability issues that must be addressed through dedicated compensation strategies without degrading the overall performance. In this work, the theoretical background, a design methodology, and experimental validation are presented. The proposed technique was applied to a fully differential amplifier built with general purpose operational amplifiers. Prior to the PSB, the amplifier exhibited a CMRR of 90 dB at 1 kHz. A straightforward application of PSB led to instability in the common-mode behavior; however, with the proposed compensation method, the amplifier achieved stable operation and an improved CMRR of 130 dB. Full article

This paper extends the quasi-load insensitive (QLI) Class-E Doherty power amplifier (PA) design methodology to address Doherty PA combiners with complex load impedance trajectories. Additionally, the QLI operation is analyzed for generalized class-E output matching networks with input series inductors and finite DC-feed inductors. We demonstrate that the QLI class-E Doherty operation can be achieved for various Doherty combiners by selecting the appropriate combination of class-E outputs matching network resonance factors and input series inductances. Moreover, a modified class-E output network is proposed to overcome the frequency limitation that might be caused by the class-E network resonance factor choice. To validate the proposed methodology, two 40 W Doherty PAs are designed and simulated using commercial GaN HEMT transistors achieving more than 70% efficiency over a 6 dB output power back-off at 3.8 GHz. Full article

This paper presents an FPGA-based configurable drive waveform generation system for industrial inkjet printheads, targeting the need for real-time adaptability in printing across heterogeneous materials. The system adopts a three-array waveform description method, enabling flexible configuration and precise generation of drive waveforms with adjustable geometry, segment duration, and voltage levels. Through a modular and parameterized design, the system supports rapid waveform switching during column-wise printing without interrupting the printing process, addressing the challenges posed by varying ink absorption characteristics across different substrates. Experimental results demonstrate the system’s capability to achieve smooth waveform transitions within an interval of 830 µs between columns, with data loading times as low as 4 µs. This efficient performance ensures seamless operation and high flexibility. The proposed approach significantly reduces the hardware cost and improves printing efficiency, offering a scalable and robust solution for next-generation industrial inkjet applications involving complex and dynamic material surfaces. Full article

This paper presents NeuroAdaptiveNet, an FPGA-based neural network framework that dynamically self-adjusts its architectural configurations in real time to maximize performance across diverse datasets. The core innovation is a Dynamic Classifier Selection mechanism, which harnesses the k-Nearest Centroid algorithm to identify the most competent neural network model for each incoming data sample. By adaptively selecting the most suitable model configuration, NeuroAdaptiveNet achieves significantly improved classification accuracy and optimized resource usage compared to conventional, statically configured neural networks. Experimental results on four datasets demonstrate that NeuroAdaptiveNet can reduce FPGA resource utilization by as much as 52.85%, increase classification accuracy by 4.31%, and lower power consumption by up to 24.5%. These gains illustrate the clear advantage of real-time, per-input reconfiguration over static designs. These advantages are particularly crucial for edge computing and embedded applications, where computational constraints and energy efficiency are paramount. The ability of NeuroAdaptiveNet to tailor its neural network parameters and architecture on a per-input basis paves the way for more efficient and accurate AI solutions in resource-constrained environments. Full article

This work presents the design of a system of a highly flexible pseudorandom number generator system (PRNG) incorporating both conventional and neuro-generators. The system integrates four internal generators with different conditions to produce new output sequences with adequate bits distribution and complexity. Two generators function at a frequency of 100 MHz with adjustable frequency settings, while two neuro-generators employ impulse neurons with distinct behaviours at 4 kHz, also modifiable. The proposed system meets 12 statistical randomness standards based on NIST’s (National Institute of Standards and Technology of U. S.) test suite, including the Frequency test, Binary Matrix Rank test, Linear Complexity test, and Random Excursion test, among others. Each resulted in a P-value greater than 0.01, confirming the pseudo-randomness of the generated sequences. The system is implemented on a reconfigurable device FPGA (Field Programmable Gate Array), with a low occupancy percentage, demonstrating its feasibility for various applications. Full article

This paper presents a novel fully integrated radio frequency (RF) rectifier tailored for a wide power dynamic range (PDR) with multiband adaptability to efficiently convert AC RF power into DC power. The proposed rectifier utilizes the strength of interstage gate biasing to achieve high power conversion efficiency (PCE) across a broad range of input power levels. Through its reconfigurable mode, the circuit seamlessly transitions between a low-power path and high-power path to ensure optimal performance across a wide PDR. Simulated using CMOS 65 nm technology, the post-layout assessment reveals a peak PCE of 48.8% at 900 MHz and 46.4% at 1800 MHz, with an extensive PDR of 20 dB for PCE exceeding 20% at both frequencies. Full article

This paper presents the design analysis of a low-power wideband single-ended CMOS low-noise amplifier (LNA). The proposed topology is based on a modified current- reuse circuit, in which two-stage common-source (CS) amplifiers consume the same DC current and are isolated from each other by large MIMCAPs, which results in good performance with low power consumption. The proposed circuit achieves a bandwidth of 2.5 GHz, suitable for several wireless communication standards such as GSM, WLAN, and Bluetooth. In the first stage, a current-reuse circuit with shunt feedback is used to satisfy input impedance matching and signal amplification with minimal noise injection. A common source (CS) with a source follower circuit forms the second stage to improve the noise figure (NF), harmonic distortion, and output impedance matching. The proposed LNA is designed in 65 nm CMOS technology and covers a frequency range of 0.17–2.68 GHz. The proposed LNA achieves a maximum gain of 17.24 dB, a minimum NF of 2.67 dB, a maximum IIP3 of −14.9 dBm, and input and output return losses of less than −10 dB. The power consumption of the proposed LNA is 3.52 mW from a 1 V power supply, and the core area is 0.3 mm². Full article

This paper presents a comprehensive review of GaAs HBT-based Doherty power amplifiers (DPAs) targeting 5G New Radio (NR) handset applications. Focusing on the critical challenges of linearity enhancement, back-off efficiency improvement, bandwidth extension under low-voltage (3.4 V) operation, and chip thermal management, the authors analyze state-of-the-art DPAs published in recent years. Key innovations including dynamic power division technique, third order intermodulation (IM3) cancellation technology, and compact output combiners are comparatively studied. Using 5G NR signals, the critical performance of the latest reported PA such as maximum linear power, back-off efficiency, bandwidth, and operating voltage are quantitatively investigated. The measurement results demonstrated that the best performance in recent DPAs achieved high linear power of 31 dBm with 34% PAE and 30 dBm with 31% PAE at the N78 and N77 bands, respectively. The corresponding adjacent channel leakage ratios (ACLRs) were lower than −36.5 dBc without digital pre-distortion (DPD). This review provides a comprehensive understanding of the latest advancements and future directions in highly efficient and linear DPA designs for 5G handset front-end modules. Full article

Modern computer applications have become highly data-intensive, giving rise to an increase in data traffic between the processor and memory units. Computing-in-Memory (CiM) has shown great promise as a solution to this aptly named von Neumann bottleneck problem by enabling computation within the memory unit and thus reducing data traffic. Many simulation tools in the literature have been proposed to enable the design space exploration (DSE) of these novel computer architectures as researchers are in need of these tools to test their designs prior to fabrication. This paper presents a collection of classical nonvolatile memory (NVM) and CiM simulation tools to showcase their capabilities, as presented in their respective analyses. We provide an in-depth overview of DSE, emerging NVM device technologies, and popular CiM architectures. We organize the simulation tools by design-level scopes with respect to their focus on the devices, circuits, architectures, systems/algorithms, and applications they support. We conclude this work by identifying the gaps within the simulation space. Full article