From Catching Up to Forging Ahead? China's New Role in the Semiconductor Industry
Bibliographic record
Abstract
China has become the largest and fastest growing semiconductor market in the world, absorbing 40% of the worldwide semiconductor shipments. For US semiconductor firms, nothing compares to the China market. China however faces a fundamental dilemma. As the world’s leading exporter of electronic products, it remains heavily dependent on imports of semiconductors and technology, primarily from the US, but also from Japan, Korea, Taiwan and Europe. At least 80 percent of the semiconductors used in China’s electronics manufacturing are imported and virtually all leading-edge devices like multi-component semiconductors (MCOs). As a result, China’s trade deficit in semiconductors has more than doubled since 2005 and now exceeds the huge amount it spends on crude oil imports. To correct this unsustainable imbalance, China’s new strategy to upgrade its semiconductor industry seeks to move from catching up to forging ahead in semiconductors through progressive import substitution. The National Semiconductor Industry Development Guidelines (Guidelines) and the Made in China 2025 (MIC 2025, 中国制造2025) plan were published by China's State Council in June 2014 and May 2015, respectively. Both policies seek to strengthen simultaneously advanced manufacturing and innovation capabilities in China’s integrated circuit (IC) design industry and its domestic IC fabrication, primarily through foundry services.Based on a review of policy documents and interviews with China-based industry experts, this paper explores how realistic these objectives are, and how this might affect international firms and the global semiconductor industry.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".