This paper presents an area-efficient$2{^{\text {nd}}}$-order noise-shaping (NS) SAR ADC leveraging a dynamic common-gate amplifier. By reconfiguring a simple switch into a dynamic common-gate (DCG) amplifier through adjusting the pulse height applied to a transistor’s gate, voltage gain is achieved prior to the passive loop filter, thereby enhancing noise transfer function (NTF) while maintaining area- and power-efficient loop filtering. To implement a$2{^{\text {nd}}}$-order loop filter, two key techniques are proposed: First, a charge-boosted amplification scheme doubles the charges transferred to the residue capacitors, enabling realization of$2{^{\text {nd}}}$-order noise shaping; Second, a dynamic bulk-switching mechanism triggers a second charge transfer by switching the amplifier’s bulk, eliminating the need for additional amplification stages. Furthermore, to ensure robust noise shaping across process-voltage-temperature (PVT) variations, a PVT-tracking pulse generator is introduced to maintain stable amplifier oper ation. With these techniques, the prototype ADC achieves a 76-dB signal-to-noise-and-distortion ratio (SNDR) with a compact active area of 0.0045 mm2. Operating at 5MS/s sample rate with a 312.5-kHz bandwidth, it consumes 31.5uW, yielding a Schreier Figure-of-Merit (FoM) of 176 dB and a Walden FoM of 9.8fJ/conv.step.

This work presents a low quantization error readout circuit with fully-charge-domain calculation for quantization and post-process of computation-in-memory (CIM)-based neural network. The contributions include: (1) A novel residual charge accumulation function is designed to achieve charge-domain summation of quantized partial sum, and reduces 38% quantization error; (2) Charge reset is introduced in the integrate & fire circuit to realize <1 LSB INL at ±7 bits and speed of 285MHz/LSB; (3) Sample & hold, current subtraction and bidirectional counter are designed to improve 3.95× energy efficiency and 2.48× area efficiency.

Non-volatile memory (NVM)-based Computation-in-memory has demonstrated a significant advantage in high-efficiency neural networks. However, the requirement of analog-to-digital converter (ADC) and post-processing circuits not only cost high energy and area but also results in high computation errors, which tradeoffs the performance boost brought by CIM. Here, we present a specific ADC and post-processing circuit of the NVM-based CIM neural network to address these issues. The main contributions include: (1) A novel residual charge accumulation function (RCA) is designed to achieve charge-domain summation of quantized partial sum and reduces 38% quantization error; (2) Charge reset is introduced in the integrate & fire circuit to realize$3.95\times $energy efficiency and$2.48\times $area efficiency. Evaluation based on the measured results of the fabricated chip shows that the VGG-11 neural network with the proposed ADC circuit can achieve a 3.28-time improvement in energy efficiency while maintaining the same network recognition rate.

This paper proposes an information-aware (IA) adaptive data acquisition (ADA) system for the Internet of Things (IoT) applications. The system can obtain valid information adaptively thanks to 1) signal-dependent full-scale feature tracks the amplitude-domain activity of the event; 2) level-crossing (LC) ADC with slope detector delivers the time-domain activity; 3) the IA algorithm determines the quantization resolution according to the detected signal activities. The proposed clock-free event-driven ADA system can reject the redundant data, and compress the valid data from the source, thus saving its power and the power of subsequent data-processing systems. The long-term average power consumption of the system is 128 nW, the resolution varies from 3 to 7 bits according to the input signal state. Compared with conventional ADCs, LC-ADC can compress the data by 2.5x [1]. Further, the proposed system has 15x higher compression ratio (CR) than that of LC-ADC.

Analog layout design is still primarily reliant on manual efforts. Current fully automated workflows are unable to meet the expectations for flexible customization and are incompatible with existing manual workflows. For both performance and productivity, interactive layout editing has the ability to bridge the gap between manual and automated flows. We present an interactive layout editing system in this study that includes well-defined commands for both placement and routing customization. This is a pioneering work that provides a holistic study on the interactive design methodology for analog layouts and its capability of speeding up design closure. Our framework comes up with the instant placement legalization and routing adjustment mechanism for rapid layout update and modification. The framework is capable of handling real-time user interaction and improving the performance of fully automated layout generators verified by post-layout simulation on real-world analog designs. Experimental results demonstrate the performance enhancement on real-world analog designs with only a few editing commands. As examples, on the low-dropout regulator, our framework can reduce the overshot down and up voltage to nearly$1/3$of layout generated by automation tool with two editing commands, and on the operational transconductance amplifier, it achieves 33.5% better common mode rejection ratio with only one command.

This paper presents a 13-tap FIR filter and an IIR filter embedded in a 10-bit SAR ADC for wireless communications chip. The IIR filter can be inherently realized through reusing the capacitor array of the SAR ADC, thus improving the stopband suppression and shaping the transition band. Besides, the DC attenuation is also avoided. The sampling rate loss of the SAR ADC can be compensated by the$4\times $time-interleaving technology. The proposed filter features high power-efficient, linearity and process compatibility. Compared with a 15-tap FIR filter, the out-of-band suppression at the cut-off frequency (OOBS@$f_{\mathrm {cut-off}}$) is enhanced by 9dB theoretically. A prototype FIR/IIR filter in 40nm CMOS occupies an active area of 0.067mm2, consumes$38~\mu \text{W}$at a single supply of 1.1V, has a 1-MHz bandwidth, obtains$>$42.2dB [email protected] when operated at 40MS/s. Meanwhile, the SAR ADC without/with the proposed filter can achieve a FoMw of 7.91 fJ/conversion-step and 13.5 fJ/conversion-step, respectively.

This article presents a clock-free spiking neural network (SNN) intelligent inference engine (IIE) for artificial intelligence of things (AIoT) sensor nodes, which often operate in random-sparse-event (RSE) scenarios. The IIE drastically reduces the system’s long-term average (LTA) power consumption, improves energy efficiency, and achieves microsecond level inference latency. Three techniques are proposed: 1) A clock-free SNN architecture without clock tree, frame generator, and arbiter, is driven by the output spikes, which are encoded with level-crossing (LC) sampling method; the circuit activity is completely related to event activity and spike rates, dramatically reducing the overall power consumption and latency. 2) The bioinspired leaky-integrate-fire (LIF) neurons directly extract the time-domain information from asynchronous spikes, reducing the network size and number of operations. 3) The computing-in-memory (CIM) and mixed-signal synapse-neuron circuits are employed to increase the SNN parallelism and avoid weight movements, thus improving the energy efficiency and response speed. The measured LTA power is bounded at 82 nW while the event-driven chip is on call and waiting for events; the energy efficiency is 0.53 pJ per synapse operation (SOP), only 1/3 that of state-of-the-art methods at 4bit weights even with 180 nm technology. We demonstrate electrocardiogram (ECG) recognition as a typical AIoT application, and the power consumption is less than 350 nW. The measured accuracy of abnormal ECG detection is 90.5%. Moreover, the latency is only$40 \mu \text{s}$to realize real-time NN inference. This work provides an effective solution for AIoT nodes that require both ultralow power and fast response.

This paper presents a dual-phase RC-based relaxation oscillator (RxO) with low temperature coefficient (TC) and high power efficiency achieved simultaneously for energy-constrained Internet-of-Things (IoT) applications with burst-mode requirements. Its circuit-level event-driven architecture reduces the duty cycle of power-hungry blocks, saving power while posing little performance penalty. In addition, the charge reuse technique further reduces the power consumption for the always-on detecting circuit. Implemented in a 0.18-μm CMOS process, the 180-kHz relaxation oscillator exhibits a frequency deviation of ± 0.26% against temperature (-40 to 125 ° C) from Monte-Carlo simulation (N=30), leading to a low temperature coefficient of 32 ppm/° C. The simulated power consumption is 163 nW, resulting in power efficiency of 0.9 nW/kHz.

This paper presents an overview for low-power successive approximation register (SAR) analog-to-digital converters (ADCs). It covers the operation principle, error analysis, and practical design issues. Furthermore, this paper provides a comprehensive survey of state-of-the-art low-power design techniques for every circuit block in the SAR ADC, including comparator, capacitive digital-to-analog converter (DAC), and SAR logic. The goal of this paper is to provide a useful overview to SAR ADC designers who want to improve the energy efficiency targeting low-to-medium speed applications.

The Internet of Things (IoT) is an interface with the physical world that usually operates in random-sparse-event (RSE) scenarios. This article discusses main challenges of IoT chips: power consumption, power supply, artificial intelligence (AI), small-signal acquisition, and evaluation criteria. To overcome these challenges, many works recently aimed at IoT system design have emerged. This work reviews the architecture and circuit innovations that have contributed to IoT developments. This paper does not cover security of IoT. Event-driven architectures and nonuniform sampling ADCs significantly reduce the long-term average power. Besides, embedding AI engines in IoT nodes (AIoT) is one critical trend. The computing-in-memory technique improves the energy efficiency of the AI engine. Asynchronous spike neural networks (ASNNs) AI engines show low power potential. In addition to data processing, small-signal acquisition is also critical. The charge-domain analog-front-end (AFE) techniques such as floating inverter-based amplifiers improve energy efficiency. In addition to the above low power and high energy efficiency technologies, energy harvesting can also enhance the lifetime of AIoT devices. This article discusses recent ambient RF and natural energy harvesting approaches and high-efficiency DC-DC with a wide load range. Finally, novel evaluation criteria are introduced to establish benchmark standards for AIoT chips.

Symmetry and matching between critical building blocks have a significant impact on analog system performance. However, there is limited research on generating system level symmetry constraints. In this paper, we propose a novel method of detecting system symmetry constraints for analog circuits with graph similarity. Leveraging spectral graph analysis and graph centrality, the proposed algorithm can be applied to circuits and systems of large scale and different architectures. To the best of our knowledge, this is the first work in detecting system level symmetry constraints for analog and mixed-signal (AMS) circuits. Experimental results show that the proposed method can achieve high accuracy of 88.3% with low false alarm rate of less than 1.1% in largescale AMS designs.

Automatic transistor sizing is a challenging problem in circuit design due to the large design space, complex performance tradeoffs, and fast technology advancements. Although there have been plenty of work on transistor sizing targeting on one circuit, limited research has been done on transferring the knowledge from one circuit to another to reduce the re-design overhead. In this paper, we present GCN-RL Circuit Designer, leveraging reinforcement learning (RL) to transfer the knowledge between different technology nodes and topologies. Moreover, inspired by the simple fact that circuit is a graph, we learn on the circuit topology representation with graph convolutional neural networks (GCN). The GCN-RL agent extracts features of the topology graph whose vertices are transistors, edges are wires. Our learning-based optimization consistently achieves the highest Figures of Merit (FoM) on four different circuits compared with conventional black box optimization methods (Bayesian Optimization, Evolutionary Algorithms), random search and human expert designs. Experiments on transfer learning between five technology nodes and two circuit topologies demonstrate that RL with transfer learning can achieve much higher FoMs than methods without knowledge transfer. Our transferable optimization method makes transistor sizing and design porting more effective and efficient.

Despite tremendous efforts in analog layout automation, little adoption has been demonstrated in practical design flows. Traditional analog layout synthesis tools use various heuristic constraints to prune the design space to ensure post layout performance. However, these approaches provide limited guarantee and poor generalizability due to a lack of model mapping layout properties to circuit performance. In this paper, we attempt to shorten the gap in post layout performance modeling for analog circuits with a quantitative statistical approach. We leverage a state-of-the-art automatic analog layout tool and industry-level simulator to generate labeled training data in an automated manner. We propose a 3D convolutional neural network (CNN) model to predict the relative placement quality using well-crafted placement features. To achieve data-efficiency for practical usage, we further propose a transfer learning scheme that greatly reduces the amount of data needed. Our model would enable early pruning and efficient design explorations for practical layout design flows. Experimental results demonstrate the effectiveness and generalizability of our method across different operational transconductance amplifier (OTA) designs.

In back-end analog/mixed-signal (AMS) design flow, well generation persists as a fundamental challenge for layout compactness, routing complexity, circuit performance and robustness. The immaturity of AMS layout automation tools comes to a large extent from the difficulty in comprehending and incorporating designer expertise. To mimic the behavior of experienced designers in well generation, we propose a generative adversarial network (GAN) guided well generation framework with a post-refinement stage leveraging the previous high-quality manually-crafted layouts. Guiding regions for wells are first created by a trained GAN model, after which the well generation results are legalized through post-refinement to satisfy design rules. Experimental results show that the proposed technique is able to generate wells close to manual designs with comparable post-layout circuit performance.

The layouts of analog/mixed-signal (AMS) integrated circuits (ICs) are dramatically different from their digital counterparts. AMS circuit layouts usually include a variety of devices, including transistors, capacitors, resistors, and inductors. A complicated AMS IC system with hierarchical structure may also consist of pre-laid out subcircuits. Different types of devices can occupy different manufacturing layers. Therefore, during the layout stage, the devices require co-optimization to achieve high circuit performance. Leveraging the fact that some devices can be built by mutually exclusive layers, they can be carefully designed to overlap each other to effectively reduce the total area and wirelength without degrading the circuit performance. In this paper, we propose an analytical framework to tackle the device layer-aware analog placement problem. Experimental results show that on average the proposed techniques can reduce the total area and half-perimeter wirelength by 9% and 23%, respectively. To verify the routability of the placement results, we also develop an analog global router, which demonstrates that the device layer-aware placement can achieve 18% shorter wirelength during global routing.