This work presents a technique for the on-chip generation of analog sinusoidal signals with high spectral quality and reduced circuitry resources. The proposed generation technique consists of a modified low-order analog filter, that provides a sinusoidal output as response to a DC input, combined with a harmonic cancellation strategy to improve the linearity of the generated signal. The proposed generator has the attributes of digital programming and control, low area overhead, and low design effort, which make this approach very suitable as test stimulus generator for built-in test applications. An integrated prototype designed in a 180 nm CMOS technology is presented in order to show the feasibility of the technique. Results obtained from the prototype show a THD around \(-80\) dB.

In this article, we leverage the power of machine learning algorithms to propose a test methodology for millimeter-wave (mm-wave) integrated circuits. The proposed test strategy is based on identifying the main process degradation mechanisms in a particular device under test (DUT) and then designing dedicated process monitor circuits to characterize this degradation and infer the DUT performance. The resulting process monitors do not load or couple to any of the DUT nodes, and the methodology can be adapted to any mm-wave device without complex codesign. The proposed test methodology is illustrated on a set of 21 fabricated samples of a 65-GHz PA designed in STMicroelectronics 55-nm CMOS technology.

This paper presents a novel Look-up Table (LUT) calibration technique for static non-linearity compensation in analog-to-digital converters (ADCs) with digital redundancy, such as Successive Approximation Register (SAR), Algorithmic, Sub-ranging or Pipeline ADCs. The method compensates the performance limitations of the conventional LUT approach in presence of comparison noise and/or non-monotonicity. In these circumstances, the input-output transfer function of a redundant ADC becomes significantly multivalued - that is, different output codes can be achieved for the same input level at different time instants. This behavior is motivated because from sample to sample, in a design with redundancy, the processing signal path is not unique, causing that the error under calibration becomes time-dependent, something which is not contemplated in the conventional calibration model. To deal with this effect, this work proposes a digital low-cost post-processing of the standardized Integral-Non-linearity (INL), which resolves multivalued situations using a direct access to the internal redundant codes. The method improvements are validated by realistic SAR and Pipeline ADC case studies at behavioral level, and by experimental data from an 11-bit 60Msps Pipeline ADC implemented in a 130nm CMOS process. These experimental results show that the proposed calibration achieves an improvement of approximately 1.6 effective bits at full-scale input amplitude.