Carbon nanotube thin-film transistors (CNT-TFTs) are promising for flexible electronics, but circuit-oriented optimization remains challenging because reliable logic operation requires a coordinated balance among multiple device metrics, and such a balance is difficult to achieve by one-factor-at-a-time tuning within a coupled multistep fabrication process. Accordingly, process optimization must be target-oriented rather than driven by any single device metric. Here, we develop a machine learning-guided process co-optimization framework for flexible CNT-TFTs and use low-voltage digital logic as a representative use case. A decision-tree model trained on more than 800 devices from 80 fabrication recipes employs a weighted evaluation score that jointly considers extracted field-effect mobility as a proxy for current-drive capability, threshold voltage, drain current at V_GS = 0 V, subthreshold swing, and on/off ratio. The optimized enhancement-mode CNT-TFTs exhibit extracted field-effect mobilities up to 71 cm² V^-1 s^-1, width-normalized drain current at V_GS = 0 V as low as 0.2 pA μm^-1, and an on/off ratio of 10⁶. Using inverters as a sensitive unit-level benchmark, we realize 2 V pseudo-PMOS inverters with a switching threshold near V_DD/2, a voltage gain of 60, and noise margins of 36%–42%. The same platform further supports a 5-stage ring oscillator operating at 60 kHz, as well as rail-to-rail basic logic gates and a 1-bit full adder, thereby verifying low-voltage dynamic and cascaded logic operation on flexible CNT-TFTs. These results show that machine learning-guided process co-optimization provides an efficient route for translating circuit requirements into fabrication strategies for flexible CNT logic circuits.

Triboelectric nanogenerators (TENGs) utilize the phenomena of contact electrification and electrostatic induction to harvest mechanical energy from the environment. A good match between the motion frequency and the circuit characteristic frequency is critical for the effective power generation of a TENG. However, most TENGs have a time-dependent inherent capacitance (TIC-TENG), which hinders an optimal design for efficient energy conversion. Here, we propose a novel structure of a TENG with a constant inherent capacitance (CIC-TENG) and a mathematical model is established to provide analytical expressions of key output parameters of the device, which gives numerical simulation results that are in good agreement with the experimentally obtained results. Figures of merit and an optimization strategy are also given as guidelines for the optimization of material selection, geometry design, etc. Furthermore, a disk-formed CIC-TENG (DCIC-TENG) with polarity-switched triboelectric pairs is constructed to harvest unidirectional mechanical energy continuously, achieving an output power density of 55 mW/m². The effects of the motion frequency, the number of electrodes and triboelectric pairs on the charge transfer efficiency of the DCIC-TENG are assessed and a preferred design strategy is given. Finally, the CIC-TENG demonstrates approximately two-fold advantages in power transfer efficiency over the TIC-TENG, and a DCIC-TENG-based self-powered anemometer was fabricated to measure wind speed in real time.