This article presents a systematic approach to generate layouts for two devices, with an arbitrary integer ratio of device sizes, that cancels up to at least third-order gradient effects. A new analysis leads to mathematical constraints on 1-D layouts that meet the required integer ratio and cancel second-order gradients. From those layouts, we apply reflection and rotation symmetries to generate 2-D layouts that cancel higher-order gradients. We demonstrate our proposed methodology on current sense transistors interspersed in active power transistors. Legato electrothermal simulation show our proposed approach, respectively, improves worst-case matching accuracy about a factor of 9.9 and 7.15 when compared to a common centroid (CC) and interdigitated (ID) pattern in the presence of gradients effects. Furthermore, we discuss evaluation metrics that can be used to select one of multiple gradient canceling layouts for any fixed rectangular grid and device application.

The extensive adoption of safety-critical applications in high-assurance environments, such as the automotive domain, has laid emphasis on safeguarding the reliability and Functional Safety (FuSa) of the Electrical and/or Electronic (E/E) components constituting such systems. Most modern automotive Systems-on-Chips (SoCs) comprise Analog and Mixed Signal (AMS) circuits, which are more susceptible to faults than their digital equivalents. However, their attributes of operating in the continuous signal region can be leveraged to perform early anomaly detection, which could facilitate the subversion of the eventual hardware failure state, thereby improving the FuSa of the system. To this end, we had proposed a novel unsupervised learning-based early anomaly detection framework catered to automotive AMS circuits (in ITC 2022). However, existing approaches to AMS FuSa violation detection are limited by pre-specified feature inputs, and lack rationale for identifying signals to be monitored to perform anomaly detection. To address these issues as well as further augment our original solution, in this paper, we propose a novel anomaly detection strategy that involves: (1) a genetic algorithm-based feature selection approach, (2) a novel signal selection algorithm that ascertains the best intermediate circuit signal, for furnishing enhanced anomaly detection accuracy, while reducing the associated detection latency, and (3) an explainable AI (XAI)-based framework that boosts user interpretability and transparency of the anomaly detection framework. This XAI approach, in turn, can be provided as feedback to the designer during circuit design and validation. The proposed approach is evaluated using case studies of two representative AMS circuits, which are prevalent in automotive SoCs. Our experimental analyses demonstrate that the proposed approach furnishes up to 100% detection accuracy and 2.3× reduction in detection time compared to our existing framework, in addition to providing insights by improving transparency of the anomaly detection framework, thereby exhibiting the efficacy of our solution.

We need to address the multifaceted challenges about silicon and system quality throughout the life cycle of silicon-based systems, spanning from design, production to in-field deployment. Innovations spanning across various parts of the silicon ecosystem are needed. In this session we invited industry experts to discuss technical trends and challenges in semiconductor industry driving a pressing need of more innovations in the emerging field of silicon lifecycle management (SLM). They will present the state-of-the-art SLM methodologies and share their perspectives of this emerging field and thoughts of future R&D directions.

On-chip testing for analog/mixed signal circuits helps improve reliability of safety-critical systems by enabling infield testing. It also alleviates the problems of increasing test costs. A low-cost high accuracy stimulus generator for on-chip spectral testing is proposed. The generator uses a low-cost DAC which requires minimal design and re-engineering efforts, in conjunction with INL based digital pre-distortion to calibrate its linearity performance. DAC output measurement, DAC INL estimation and DAC linearity calibration are all performed on-chip. Measurement results in 40nm bulk CMOS technology demonstrate that the circuit is capable of producing a rail-to-rail differential signal with THD of -75 dB and SFDR of 79dB. The proposed solution is a major step forward in demonstrating the feasibility of synthesizable built-in-test solutions for high-accuracy embedded analog and mixed signal functions.

This paper presents a complete on-chip ADC BIST solution based on a segmented stimulus error identification algorithm known as USER-SMILE. By adapting the algorithm for efficient hardware realization, the solution is implemented towards a 1Msps 12-bit SAR ADC on a 28nm CMOS automotive microcontroller. While sufficient test accuracy is demonstrated, the solution is further extended to correct linearity errors of ADC. The entire BIST and calibration circuitry occupies 0.028mm2silicon area while enabling more than 10 times tester time reduction and >10dB THD/SFDR performance improvement over an existing structural capacitor-weight-identification calibration scheme. The added die cost is estimated to be 1/8 of the saved test cost from tester time reduction alone.

Linearity test is a fundamental test for ADCs in production. The stringent linearity requirement for an on-chip signal generator has made ADC built-in-self-test (BIST) solutions prohibitive in the past. The stimulus error identification and removal (SEIR) method has greatly reduced the linearity requirement. However, it still requires the addition of a highly stable voltage offset, which remains a daunting task. To solve this problem, this paper proposes a simple and low-power method to inject the required constant offset. It exploits the inherent capacitive sample-and-hold circuit used in various ADC architectures. It ensures the injected offset to have a very high constancy, which results in an accurate INL estimation. A 16-bit SAR ADC with the proposed BIST scheme is modeled and simulated in Matlab to prove its validity. The results show that the estimation error on the maximum INL is less than 0.07 LSB.