Silicon Millimeter-Wave, Terahertz, and High-Speed Fiber-Optic Device and Benchmark Circuit Scaling Through the 2030 ITRS Horizon
Bibliographic record
Abstract
This paper reviews the technology requirements of future 100-300-GHz millimeter-wave (mm-wave) systems-on-chip (SOI) for high data rate wireless and sensor applications, as well as for 100-300-GBaud fiber-optic communication systems. Measurements of state-of-the-art silicon metal-oxide-semiconductor field-effect transistors (MOSFETs), SiGe heterojunction bipolar transistors (HBTs), and of a variety of HBT-HBT and MOS-HBT cascodes are presented from dc to 325 GHz. The challenges facing mm-wave MOSFET and SiGe HBT device and benchmark circuit scaling toward 2-3-nm gate length and beyond 2-THz transistor Fmaxare discussed for the first time based on technology computer-aided design (TCAD) and atomistic simulations. Finally, simulations of the scaling of the SiGe HBT analog and mixed-signal mm-wave benchmark circuit performance across future technology nodes predict that PAs with 45% power added efficiency (PAE) at 220 GHz, track and hold amplifiers (THAs) with over 140-GHz bandwidth, and transimpedance amplifiers (TIAs) with 250-GHz bandwidth and less than 5-dB noise figure will become feasible by 2030. Comparison of simulations and measurements for representative benchmark circuits such as TIAs, THAs, linear modulator drivers, digital-to-analog converters (DACs), and power amplifiers (PAs), fabricated in advanced SiGe BiCMOS and nanoscale SOI complementary metal-oxide-semiconductor (CMOS) technologies, and operating at 120 Gb/s and above 100 GHz, respectively, are presented to support the credibility of the benchmark circuit scaling exercise.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".