arXiv subjects

Ali M. Niknejad

Publications and source records attributed to Ali M. Niknejad.

5 recordsLinked to original sources

arXiv2026

A Learning Method for Optimal Chirp Predistortion

This article introduces the "chart & chirp" method of one-shot, in situ VCO tuning curve estimation, learning, and predistortion, which provides an alternative to prevailing LMS-based background calibration loops. The proposed approach utilizes a cycle-counting FDC to estimate the VCO tuning curve and a linearly interpolating QDAC to actuate the predistortion. An LLSE-based learning process generates predistorted DAC control codes for any physically achievable chirp parameters. In simulation, 50 and 51 kHz of RMS FM error is achieved for 5 and 20 us chirps, respectively. The locations of nonlinearity-induced spurs in the IF spectrum are predicted via analysis of the DFT of FM error in generated chirps, with higher frequency DAC updates weakening these spurs and moving them out of the IF band of interest. Simulation in a coherent, monostatic radar model reveals that deterministic phase error from the chirp nonlinearity arises at a level above -70 dBc/Hz at 1 MHz offset, dominating random phase noise and setting the IF SNDR for R < 23 m and R = 92 m around 40 dB and 24 dB, respectively.

eess.SP↗

arXiv2026

Theoretical Studies of Sub-THz Active Split-Ring Resonators for Near-Field Imaging

This paper develops a theoretical framework for the design of Active Split-Ring Resonators (ASRRs). An ASRR is a Split-Ring Resonator (SRR) equipped with a tunable negative resistor, enabling both switchability and quality factor boosting and tuning. These properties make ASRRs well-suited for integration into dense arrays on silicon chips, where pixelated near-fields are generated and leveraged for high-resolution 2D imaging of samples. Such imagers pave the way for real-time, non-invasive, and low-cost imaging of human body tissue. The paper investigates ASRR coupling to host transmission lines, nonlinear effects, signal flow, and the influence of various noise sources on detection performance. Verified through simulations, these studies provide design guidelines for optimizing the Signal-to-Noise Ratio (SNR) and power consumption of a single pixel, while adhering to the constraints of a scalable array.

eess.SY↗

arXiv2025

Configurable γ Photon Spectrometer to Enable Precision Radioguided Tumor Resection

Surgical tumor resection aims to remove all cancer cells in the tumor margin and at centimeter-scale depths below the tissue surface. During surgery, microscopic clusters of disease are intraoperatively difficult to visualize and are often left behind, significantly increasing the risk of cancer recurrence. Radioguided surgery (RGS) has shown the ability to selectively tag cancer cells with gamma (γ) photon emitting radioisotopes to identify them, but require a mm-scale γ photon spectrometer to localize the position of these cells in the tissue margin (i.e., a function of incident γ photon energy) with high specificity. Here we present a 9.9 mm2 integrated circuit (IC)-based γ spectrometer implemented in 180 nm CMOS, to enable the measurement of single γ photons and their incident energy with sub-keV energy resolution. We use small 2 2 um reverse-biased diodes that have low depletion region capacitance, and therefore produce millivolt-scale voltage signals in response to the small charge generated by incident γ photons. A low-power energy spectrometry method is implemented by measuring the decay time it takes for the generated voltage signal to settle back to DC after a γ detection event, instead of measuring the voltage drop directly. This spectrometry method is implemented in three different pixel architectures that allow for configurable pixel sensitivity, energy-resolution, and energy dynamic range based on the widely heterogenous surgical and patient presentation in RGS. The spectrometer was tested with three common γ-emitting radioisotopes (64Cu, 133Ba, 177Lu), and is able to resolve activities down to 1 uCi with sub-keV energy resolution and 1.315 MeV energy dynamic range, using 5-minute acquisitions.

eess.IV↗

arXiv2024

A Wireless, Multicolor Fluorescence Image Sensor Implant for Real-Time Monitoring in Cancer Therapy

Real-time monitoring of dynamic biological processes in the body is critical to understanding disease progression and treatment response. This data, for instance, can help address the lower than 50% response rates to cancer immunotherapy. However, current clinical imaging modalities lack the molecular contrast, resolution, and chronic usability for rapid and accurate response assessments. Here, we present a fully wireless image sensor featuring a 2.5$\times$5 mm$^2$ CMOS integrated circuit for multicolor fluorescence imaging deep in tissue. The sensor operates wirelessly via ultrasound (US) at 5 cm depth in oil, harvesting energy with 221 mW/cm$^{2}$ incident US power density (31% of FDA limits) and backscattering data at 13 kbps with a bit error rate <$10^{-6}$. In-situ fluorescence excitation is provided by micro-laser diodes controlled with a programmable on-chip driver. An optical frontend combining a multi-bandpass interference filter and a fiber optic plate provides >6 OD excitation blocking and enables three-color imaging for detecting multiple cell types. A 36$\times$40-pixel array captures images with <125 $μ$m resolution. We demonstrate wireless, dual-color fluorescence imaging of both effector and suppressor immune cells in ex vivo mouse tumor samples with and without immunotherapy. These results show promise for providing rapid insight into therapeutic response and resistance, guiding personalized medicine.

physics.med-ph↗

arXiv2020

Compact Device Models for FinFET and Beyond

Compact device models play a significant role in connecting device technology and circuit design. BSIM-CMG and BSIM-IMG are industry standard compact models suited for the FinFET and UTBB technologies, respectively. Its surface potential based modeling framework and symmetry preserving properties make them suitable for both analog/RF and digital design. In the era of artificial intelligence / deep learning, compact models further enhanced our ability to explore RRAM and other NVM-based neuromorphic circuits. We have demonstrated simulation of RRAM neuromorphic circuits with Verilog-A based compact model at NCKU. Further abstraction with macromodels is performed to enable larger scale machine learning simulation.

cs.ET↗