Domain-Adaptive Transfer Learning Framework for Multimodal Technical Communication in VLSI Engineering
K Subhamraj Patra, G Sai Chaitanya, Minakhi Dash, Rosysmita Bikram Singh · 2025
Technical communication within Very Large-Scale Integration (VLSI) engineering encompasses both precise writing and clear oral presentations, forming a cornerstone for successful design reviews and stakeholder collaboration. Transfer learning and domain adaptation techniques offer powerful mechanisms to tailor pretrained language and speech models to the specialized vocabulary and acoustic nuances of VLSI workflows. This study introduces a unified, dual-modal pipeline that begins with the construction of a large-scale annotated corpus comprising 5,000 design specifications, verification reports, and 200 recorded presentations. A transformer-based language model undergoes two-stage fine-tuning: initial adaptation on general engineering texts followed by specialization on VLSI documents. Concurrently, an adversarial acoustic encoder is trained with gradient reversal to produce domain-invariant representations of spoken content, paired with a scoring head that predicts clarity metrics. Evaluation employs automated measures—including grammatical error rate (GER) and domain term usage precision—alongside expert rubric scores for clarity and fluency. Results demonstrate a 28.8% reduction in GER, a 23.8% increase in term usage precision, a 31.3% improvement in presentation clarity, and an 11.4% gain in fluency. Ablation studies confirm that VLSI-specific fine-tuning delivers the largest marginal gains, while cross-modal correlation (r = 0.61, p < 0.001) indicates that enhancements in written communication align with verbal performance gains. Inference latencies remain under 60 ms per sample, supporting integration into real-time pipelines. Findings validate the effectiveness of adversarial domain adaptation in elevating both written and spoken technical communication, with potential applications in engineering education and industrial design workflows. Future work will explore user feedback loops and cross-industry transferability.