In this paper, a Low-Temperature Polycrystalline Oxide (LTPO) Thin Film Transistors (TFTs) based power management integrated circuits (PMICs), consisting of both charge pump and low dropout regulator (LDO), are presented for the first time. A charge pump circuit is proposed to convert the supply voltage into both negative and doubled positive voltages with a minimum area overhead, which benefits from a switch-reused hybrid architecture and integration of both P and N type TFTs. A high performance LDO with a fixed-factor feedback network is proposed to reduce the output offset caused by the intrinsic process variation of flexible TFTs. The charge pump has an 8 V input, with outputs of +15.8 V and -7.8 V. With a load current range of 20 µA to 300 µA, the voltage drop across the charge pump remains below 1.2 V, with efficiency consistently exceeding 90% and peaking at 96.5%. The proposed LDO has a 4.53 V output, achieving an ultra-low quiescent current of 311 nA and delivering a load current of 11.6 mA.

Design technology co-optimization (DTCO) plays a critical role in achieving optimal power, performance, and area (PPA) for advanced semiconductor process development. Cell library characterization is essential in DTCO flow, but traditional methods are time-consuming and costly. To overcome these challenges, we propose a graph neural network (GNN)-based machine learning model for rapid and accurate cell library characterization. Our model incorporates cell structures and demonstrates high prediction accuracy across various process-voltage-temperature (PVT) corners and technology parameters. Validation with 512 unseen technology corners and over one million test data points shows accurate predictions of delay, power, and input pin capacitance for 33 types of cells, with a mean absolute percentage error (MAPE) ≤ 0.95% and a speedup of 100X compared with SPICE simulations. Additionally, we investigate system-level metrics such as worst negative slack (WNS), leakage power, and dynamic power using predictions obtained from the GNN-based model on unseen corners. Our model achieves precise predictions, with absolute error ≤ 3.0 ps for WNS, percentage errors ≤ 0.60% for leakage power, and ≤ 0.99% for dynamic power, when compared to golden reference. With the developed model, we further proposed a fine-grained drive strength interpolation methodology to enhance PPA for small-to-medium-scale designs, resulting in an approximate 1-3% improvement.

This paper proposes a fast system technology co-optimization (STCO) framework that optimizes power, performance, and area (PPA) for next-generation IC design, addressing the challenges and opportunities presented by novel materials and device architectures. We focus on accelerating the technology level of STCO using AI techniques, by employing graph neural network (GNN)-based approaches for both TCAD simulation and cell library characterization, which are interconnected through a unified compact model, collectively achieving over a 100X speedup over traditional methods. These advancements enable comprehensive STCO iterations with runtime speedups ranging from 1.9X to 14.1X and supports both emerging and traditional technologies.

The robotic system is developed to improve the effect of microsurgery for keratoplasty. The autonomous suturing should be qualified for the operational requirements of microsurgical keratoplasty. Poorly modeled mechanism of robotic micromanipulator and slight movements of surgical objective point are hindrance for precise position and orientation of end-needle. Visual servo control is available to overcome these obstacles. An appropriate scheme of robotic vision is proposed. On the basis of biological binocular vision, a feasible method of calibration and reconstruction for surgical microscope is adopted. The model parameters estimated by linear regression are evaluated for accuracy, stability and robustness. The visual servo control is applied for guiding robotic end-needle to reach the penetrating objective point. The visual servo control has look-and-move architecture based on image feature. The experimental results show that the robotic system for microsurgical keratoplasty can fulfil the surgical task of suturing penetration precisely