Semiconductor design often involves balancing competing performance metrics, where improving one parameter may degrade another. For example, high carrier mobility is desirable for fast switching but may increase leakage currents, while wide bandgap materials reduce leakage but can limit mobility. Traditional trial-and-error approaches struggle to navigate these trade-offs efficiently. Artificial intelligence (AI) techniques, particularly multi-objective optimization and machine learning (ML), provide systematic methods to explore optimal compromises between conflicting requirements.

One key method for analyzing trade-offs is Pareto front analysis, which identifies non-dominated solutions where no single objective can be improved without worsening another. In semiconductor design, this translates to evaluating material compositions, doping profiles, or device architectures that balance metrics like on-current, off-current, and subthreshold swing. For instance, tunnel field-effect transistors (TFETs) require simultaneous optimization of bandgap engineering for high Ion/Ioff ratios and interface defect minimization for steep subthreshold slopes. A Pareto front can reveal the best-performing heterostructure configurations—such as InAs/GaSb broken-gap designs—where further improvements in tunneling efficiency would compromise electrostatic control.

Genetic algorithms (GAs) are another powerful tool for exploring high-dimensional trade-offs. These evolutionary algorithms generate populations of candidate solutions, evaluate their fitness across multiple objectives, and iteratively recombine high-performing traits. In III-V semiconductor design, GAs have been applied to optimize ternary or quaternary alloys (e.g., InGaAs, AlGaN) for specific device applications. A GA might explore the composition space of InxGa1-xAs to maximize electron mobility while minimizing trap-assisted leakage, with constraints on lattice mismatch to avoid dislocation formation. The algorithm evaluates thousands of compositions, favoring those that achieve the best compromise between electrical performance and structural reliability.

Machine learning accelerates trade-off optimization by predicting material properties without costly simulations or experiments. Neural networks trained on ab initio calculations or experimental datasets can map compositional gradients to device performance. For example, ML models have been used to design graded-channel heterostructures in high-electron-mobility transistors (HEMTs), where the Al fraction in AlGaN is varied to minimize current collapse while preserving high mobility. By learning the relationship between Al concentration profiles and key metrics like 2DEG density and breakdown voltage, ML identifies grading schemes that outperform uniform compositions.

Bayesian optimization is particularly effective for expensive-to-evaluate objectives, such as semiconductor process parameters requiring fabrication runs. It models uncertainty in predictions and prioritizes experiments likely to improve Pareto-optimal solutions. In gate stack engineering, Bayesian methods have optimized high-k dielectric thickness and metal gate work functions to balance gate leakage and threshold voltage stability. The algorithm sequentially selects deposition conditions that maximize the probability of advancing the Pareto front, reducing the number of experimental iterations needed.

Multi-task learning frameworks address trade-offs by jointly training models on correlated semiconductor properties. For example, a deep learning model might predict both hole mobility and defect formation energy in p-type oxides, recognizing that oxygen vacancies enhance conductivity but degrade reliability. The model’s shared representations capture underlying physical relationships, enabling co-optimization of these linked parameters. This approach has been applied to organic semiconductors, where backbone rigidity improves charge transport but reduces solubility for processing.

Active learning integrates AI with experimental feedback for adaptive optimization. In molecular beam epitaxy (MBE), ML models suggest growth temperatures and flux ratios to achieve target alloy uniformity and doping profiles. After each growth run, characterization data (e.g., XRD, Hall measurements) refine the model’s predictions, progressively steering parameters toward the Pareto front. This closed-loop approach has demonstrated rapid optimization of GaN p-type doping, where Mg incorporation efficiency competes with crystalline quality.

Challenges remain in ensuring AI-generated solutions are physically realizable. Constrained optimization techniques incorporate fabrication limits, such as thermal budget constraints in anneal processes or etch selectivity bounds in nanostructuring. Reinforcement learning agents trained on process-design kits (PDKs) can navigate these constraints while optimizing device performance. For example, in FinFET design, an RL agent might adjust fin width and doping to meet both drive current and leakage specifications within lithography resolution limits.

Cross-property surrogate models enable efficient exploration of complex trade-offs. By approximating density functional theory (DFT) or TCAD simulations, these models rapidly evaluate millions of candidate designs. A surrogate trained on III-nitride datasets could predict how In composition in InGaN quantum wells affects both radiative recombination rates and piezoelectric strain, guiding LED designs that balance efficiency and droop mitigation.

The integration of these AI techniques into semiconductor development pipelines is transforming how engineers navigate multi-objective trade-offs. By systematically mapping Pareto fronts, leveraging evolutionary exploration, and learning from data, AI accelerates the discovery of materials and devices that optimally balance competing requirements. Future advancements will likely focus on hybrid AI-physics models that combine deep learning with fundamental semiconductor equations, further enhancing predictive accuracy and interpretability. As fabrication technologies advance, AI-driven co-optimization will become indispensable for designing next-generation semiconductors with tightly coupled performance metrics.