Electronics

This paper presents transistor-level building blocks for fractional-N frequency synthesis in 45 nm CMOS. The main contribution is a five-stage differential current-starved ring VCO using gate-to-body-connected dynamic-threshold MOS devices and sleep-stack transistors placed only in alternate stages to limit the added delay and capacitive loading. The digital section includes an eight-stage multi-modulus frequency divider based on push-pull cascode logic divide-by-2/3 cells and a MASH 1-1-1 sigma-delta controller. All circuits are implemented in Cadence Virtuoso with a nominal 1.0 V supply. Post-layout simulation of the VCO gives an oscillation frequency of 9.24 GHz, phase noise of −124.38 dBc/Hz at a 1 MHz offset, and power consumption of 59.4 µW, corresponding to an FoM of −215.96 dBc/Hz. At $V_{\mathrm{c}\mathrm{t}\mathrm{r}\mathrm{l}}=0.5$ V, the extracted VCO remains oscillatory over 60 combined process, supply voltage, and temperature conditions, with the oscillation frequency ranging from 6.43 to 10.96 GHz. The MMFD, MASH controller, and their integrated fractional-division path are evaluated under the reported nominal operating conditions; PVT robustness of the combined digital path is not established in this work. The digital-path validation includes analysis of fractional-divider command convergence, edge-count consistency, output line spectra, and characterization of the third-order noise-transfer response. The work focuses on the design and validation of individual frequency-synthesis building blocks rather than a complete closed-loop fractional-N PLL.

As programmable photonic integrated circuits scale toward higher port counts and system-level reconfigurability, high-density electrical fan-out is becoming a major packaging bottleneck. Reliable Au-wire bonding to AlCu redistribution structures depends strongly on upstream wafer fabrication, although optimization often focuses on final bonding parameters. This study presents a failure-guided process-integration strategy for Ti/AlCu silicon fan-out submounts. A root-cause analysis of non-stick-on-pad (NSOP) failures was used to establish two reworkable in-line acceptance gates before irreversible assembly, covering the AlCu pattern-transfer fidelity, residual metal clearance, passivation-window integrity, and bond-pad exposure. With thermosonic bonding parameters held constant, the implementation of these gates and associated corrective actions increased the mean wire-pull force from 3.18 gf (31.2 mN) to 10.46 gf (102.6 mN), a 3.29-fold increase. All 30 measurements from the monitored-process batch exceeded the project-specific acceptance criterion of 4.3 gf (42.2 mN), and the dominant failure mode shifted from bond-pad interfacial separation to wire-neck or wire-body fracture. The optimized route enabled the high-pad-count assembly and functional verification of a programmable photonic device. Because the comparison comprised one fabrication batch per route with 30 bond-level pull tests per batch, the observed improvement is interpreted as a batch-specific process–response association rather than evidence of wafer-to-wafer or lot-to-lot reproducibility.

The practical value of quantum machine learning for network anomaly detection remains unclear under controlled and comparable benchmarking conditions. This study evaluates a trash-qubit-based Quantum Autoencoder (QAE) against seven classical anomaly-detection methods on UNSW-NB15 using shared data partitions, preprocessing steps, model selection procedures, and threshold calibration protocols. The experimental design included five independent seeds, three training set sizes, two feature budgets, and 12 QAE architectures, together with low-FPR analysis, finite-shot and synthetic-noise evaluation, less-constrained classical reference settings, and a cross-dataset assessment on TON_IoT. The Angle-RY encoding with a Real-Amplitudes Ring ansatz at depth L = 2 achieved the highest mean validation-selection AUPRC. On UNSW-NB15, the QAE was competitive in AUPRC and AUROC but remained behind the strongest classical baselines in F1-score, and its attack recall declined sharply under low-FPR constraints. The TON_IoT results further showed that relative model performance depended on the dataset. Under finite-shot and synthetic-noise conditions, anomaly-score rankings were largely preserved, whereas threshold-based decisions were more sensitive. Overall, the QAE showed competitive continuous-score discrimination on UNSW-NB15, but achieving reliable detection at low false-alarm rates remains a key operational challenge.