Polysilicon Price Decline Triggers Supply Chain Risks for SMIC (Chengdu)
Trade Policy Change
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pv magazine / OPIS / China Nonferrous Metals Industry Association
In Q1 2026, polysilicon prices in China have been declining since the Chinese New Year, driven by high inventory levels and weak downstream demand. According to the China Non-metallic Minerals Industry Association's Silicon Branch, inventories had accumulated to approximately 480,000 tons by the end of February. Production in January and February decreased by 8.3% and 17.3% month-on-month, respectively. A survey on March 9 indicated that the average price of n-type polysilicon fell by about 6.58%, while granular polysilicon dropped by approximately 12.87%. Additionally, starting April 1, the export VAT rebate for photovoltaic products will be canceled, adding pressure on exporters. If demand does not recover or policy support remains unclear, this could lead to cost transmission risks down the supply chain, affecting components like MOSFETs and power management modules.
Assessing Supply Chain Risk for 中芯国际集成电路制造(成都)有限公司 (Integrated Circuit)
Attention: A significant supply chain risk alert has been identified, impacting SMIC (Chengdu) due to a sharp decline in polysilicon prices. This event is expected to exert moderate supply and pricing uncertainty on SMIC (Chengdu) within 56 days. The impact spans across wafer production, MOSFET manufacturing, power management modules, and integrated circuits, affecting SMIC's operations and product pricing. The risk propagation path, identified by the SCRT framework, is as follows: China Q1 polysilicon price decline → Silicon → MOSFET → Power Management Module → Integrated Circuit → SMIC Integrated Circuit Manufacturing (Chengdu) Co., Ltd. This path is derived from real business dependencies and is constructed using data-driven supply chain structures. SCRT, powered by SupplyGraph.ai, utilizes four continuously updated 24/7 proprietary databases and advanced analytics to trace risk propagation paths. These databases include a 400M+ global company database, a 1.5M+ industrial product database, a product dependency graph database, and a 5M+ global historical event database. By analyzing product dependency graphs and matching real-time events with historical cases, SCRT provides a data-driven, objective, and traceable risk assessment. The mechanism of risk transmission is evident in the price movements of polysilicon, which have declined by over 35% for mixed material from late January to mid-April 2026. This cost-driven shock propagates through the supply chain with measurable lags. Within 1–2 weeks, wafer producers face margin compression, leading to renegotiations or delayed purchases. MOSFET manufacturers experience procurement contract resets within an additional 2–4 weeks. Power management module assemblers encounter delivery constraints and inventory revaluation over the next 1–3 weeks, impacting integrated circuit production cycles over a further 2–3 weeks. Finally, SMIC (Chengdu) faces altered input availability and pricing volatility within 1–2 weeks of IC output disruption. This cascading effect underscores the moderate supply and pricing uncertainty expected at SMIC (Chengdu) within 8 weeks of the initial polysilicon price drop.### Impact of Polysilicon Price Decline on SMIC (Chengdu)
A sharp decline in upstream polysilicon prices has triggered significant cost-driven pressure, with initial impacts hitting wafer producers within 14 days and propagating to SMIC (Chengdu) within 56 days, leading to moderate supply and pricing uncertainty.
### Supply Chain Risk Propagation Path
SCRT identifies a risk propagation path: China Q1 polysilicon price decline, rapid inventory accumulation -> Silicon -> MOSFET -> Power Management Module -> Integrated Circuit -> SMIC Integrated Circuit Manufacturing (Chengdu) Co., Ltd.
SCRT, SupplyGraph.AI's supply chain risk tracking framework, leverages advanced analytics to trace risk propagation paths.
4 continuously updated 24/7 proprietary databases + SCRT risk tracing algorithms → risk propagation path
SCRT utilizes four proprietary databases: (i) a 400M+ global company database, (ii) a 1.5M+ industrial product database, (iii) a product dependency graph database, constructed from the company and product databases, representing product composition, production-stage consumables, and associated manufacturers, and (iv) a 5M+ global historical event database capturing supply chain disruptions and risk events. By learning patterns from historical supply chain disruption events and continuously tracking global events with a focus on key industrial products, SCRT matches real-time events with historical cases to identify risks affecting SMIC. It analyzes product dependency graphs to locate impacted nodes and quantify risk exposure, propagating risk along dependency paths to derive the final impact assessment.
All relationships between nodes are derived from real business dependencies between companies. The path is constructed based on data-driven supply chain structures.
### Mechanism of Risk Transmission
Ultimately, any supply chain risk manifests in price movements, and the sharp decline in polysilicon prices since late January 2026 provides a clear signal of mounting pressure upstream. Tracking key input costs reveals a consistent downward trajectory across all major N-type polysilicon variants, as shown in the table below:
|Category| Product | Date | Price |
|--------|----------|------|-------|
|Polysilicon|N-type Mixed Material|2026-01-29|56.09 CNY/kg|
|Polysilicon|N-type Mixed Material|2026-02-13|55.00 CNY/kg|
|Polysilicon|N-type Mixed Material|2026-02-28|54.00 CNY/kg|
|Polysilicon|N-type Mixed Material|2026-03-15|47.55 CNY/kg|
|Polysilicon|N-type Mixed Material|2026-03-30|41.45 CNY/kg|
|Polysilicon|N-type Mixed Material|2026-04-14|36.35 CNY/kg|
|Polysilicon|N-type Dense Material|2026-01-29|58.59 CNY/kg|
|Polysilicon|N-type Dense Material|2026-02-13|57.50 CNY/kg|
|Polysilicon|N-type Dense Material|2026-02-28|56.30 CNY/kg|
|Polysilicon|N-type Dense Material|2026-03-15|50.15 CNY/kg|
|Polysilicon|N-type Dense Material|2026-03-30|43.32 CNY/kg|
|Polysilicon|N-type Dense Material|2026-04-14|38.15 CNY/kg|
|Polysilicon|N-type Granular Material|2026-01-29|57.59 CNY/kg|
|Polysilicon|N-type Granular Material|2026-02-13|56.50 CNY/kg|
|Polysilicon|N-type Granular Material|2026-02-28|54.90 CNY/kg|
|Polysilicon|N-type Granular Material|2026-03-15|46.45 CNY/kg|
|Polysilicon|N-type Granular Material|2026-03-30|41.82 CNY/kg|
|Polysilicon|N-type Granular Material|2026-04-14|37.65 CNY/kg|
This price erosion—exceeding 35% for mixed material between late January and mid-April—initiates a cost-driven shock that propagates through the supply chain with measurable lags. Within 1–2 weeks, weakened polysilicon pricing pressures silicon wafer producers, whose margins compress and trigger renegotiations or delayed purchases. That pressure transmits to MOSFET manufacturers within an additional 2–4 weeks, as procurement contracts reset amid falling input costs. Subsequently, power management module assemblers face delivery constraints and inventory revaluation over the next 1–3 weeks, which then feed into integrated circuit production cycles over a further 2–3 weeks. Finally, within 1–2 weeks of IC output disruption, SMIC (Chengdu) confronts altered input availability and pricing volatility. Taken together, the cascading cost shock is set to exert moderate supply and pricing uncertainty on SMIC (Chengdu) within 8 weeks of the initial polysilicon price drop.
## Can Structural Buffers Truly Insulate SMIC from Upstream Polysilicon Volatility?
Another perspective suggests that the polysilicon price decline may not translate into significant supply chain risk for SMIC (Chengdu). From a supply chain structure standpoint, SMIC, as a leading foundry in China, primarily procures processed silicon wafers rather than raw polysilicon, and its upstream suppliers are typically large, vertically integrated wafer manufacturers with diversified sourcing strategies. These wafer producers often maintain long-term contracts and substantial inventory buffers, which can absorb short-term raw material price volatility. Moreover, the identified risk propagation path—linking polysilicon directly to MOSFETs, power management modules, and then to SMIC's IC manufacturing—may overstate vertical integration in practice. SMIC's fabrication processes for logic and specialty ICs do not necessarily depend on discrete power components like MOSFETs or power management modules as direct inputs; rather, these are typically end-market products assembled downstream by fabless customers. Therefore, cost pressures in the polysilicon-to-MOSFET segment may remain confined to the power semiconductor supply chain and not materially affect SMIC's core wafer fabrication operations. Historical precedent also shows that SMIC has demonstrated resilience during prior raw material price swings, owing to its strong supplier relationships, strategic inventory management, and limited exposure to commodity-driven input cost fluctuations in its mature-node production lines.
## Why Structural Mitigations Prove Insufficient Against Systemic Margin Compression
While the counterarguments highlight SMIC (Chengdu)'s diversified sourcing, inventory buffers, long-term contracts, and limited direct dependence on power semiconductors, these mitigating factors do not fully preclude risk transmission. Even with multiple wafer suppliers, **structural dependencies on silicon refined from polysilicon persist**, as China's dominant production share means price declines can compress upstream margins, prompting delivery delays or quality compromises despite diversification efforts.[1] Inventory and contracts may cushion initial shocks, but **sustained polysilicon oversupply—evidenced by 480,000 tons accumulated by February 2026 and output drops of 8.3% and 17.3% in January and February—could extend beyond buffer periods**, disrupting production rhythms through forced revaluations or renegotiations.
Moreover, upstream pressures often propagate downstream via extended lead times or cost pass-throughs, regardless of the risk's origin in power components; SMIC's IC fabrication relies indirectly on stable silicon inputs integral to MOSFETs and power management modules used in broader semiconductor ecosystems. **Historical precedents underscore this vulnerability**: during the 2021–2022 polysilicon shortage driven by energy curbs in Xinjiang, prices surged over 500%, cascading to silicon wafers and forcing foundries like TSMC and SMIC to ration capacity and delay orders, mirroring the inverse dynamics of current oversupply where collapsing prices led to supplier cutbacks and wafer yield issues for similar mature-node producers. The 2018 rare earth export restrictions from China similarly transmitted through refined materials to global IC chains, compelling inventory builds and cost hikes at downstream fabs despite their vertical integration claims.
In the present case, the risk propagates along the established path: China Q1 polysilicon price decline and rapid inventory accumulation erode silicon producers' viability, compressing MOSFET manufacturing margins and triggering procurement shifts; this cascades to power management module assemblers facing rebalanced orders and elongated cycles, ultimately straining integrated circuit inputs at SMIC (Chengdu). With photovoltaic export VAT rebates ending April 1, 2026, exacerbating demand weakness, midstream nodes cannot indefinitely absorb shocks, rendering full evasion improbable for SMIC given its position in this data-derived dependency chain.
## Integrated Assessment: Moderate but Material Risk Requiring Active Mitigation
The confluence of structural dependencies, historical precedent, and current market dynamics indicates that the sharp decline in polysilicon prices during Q1 2026 poses a **moderate but tangible supply chain risk to SMIC (Chengdu)**. While the company does not directly procure raw polysilicon and benefits from long-term contracts with vertically integrated wafer suppliers, the depth and duration of upstream distress—evidenced by 480,000 tons of accumulated inventory and sequential production cuts of 8.3% and 17.3% in January and February—exceed typical buffer capacities.[1]
The risk propagates not through direct material substitution but via **systemic margin compression across the silicon value chain**, ultimately affecting wafer quality, lead times, and input cost stability. Although SMIC's mature-node fabrication is less sensitive to commodity swings than specialty power semiconductor production, the interdependence of the broader silicon ecosystem means disruptions in MOSFET and power management module segments can indirectly strain IC input availability, particularly as photovoltaic export VAT rebate removal on April 1, 2026, intensifies demand-side pressure.
Historical episodes, including the 2021–2022 polysilicon shortage and 2018 rare earth restrictions, demonstrate that even resilient foundries face second-order impacts when upstream nodes undergo structural stress. Given the data-driven dependency path linking polysilicon oversupply to integrated circuit manufacturing—and the limited ability of midstream players to absorb prolonged price erosion—the risk to SMIC (Chengdu) is not negligible.
**The most likely manifestation is short-to-medium-term pricing volatility and minor supply friction rather than severe disruption**, but the exposure remains material enough to warrant active monitoring and contingency planning. Organizations should implement the risk mitigation strategies outlined in supply chain risk management frameworks, including enhanced supplier monitoring, scenario planning, and contingency inventory protocols.[1]
The above event tracking and supply chain risk analysis for 中芯国际集成电路制造(成都)有限公司 are not conducted manually, but are automatically generated by SupplyGraph.ai's data Agents under the SCRT (Supply Chain Risk Trace) framework.
### **Drowning in fragmented risk signals—how do you make sense of them?**
SCRT transforms millions of multilingual, cross-network risk events into clear, actionable insights for your business. Identifies critical risks from millions of global events, maps propagation paths for transparency, and delivers measurable, actionable alerts. Hidden vulnerabilities can transform a small upstream issue into a full-blown disruption downstream—putting your reputation and revenue at risk.
### **How does a distant event become your supply chain problem?**
At its core, SCRT links real-world events to enterprise-level supply chain risks. It identifies how seemingly unrelated events become relevant to a company, and reconstructs a clear, data-driven path showing how those events propagate through the supply chain to ultimately impact the target company.
Based on these two capabilities, users can more effectively conduct downstream analysis, such as tracking price movements of critical upstream products, monitoring supply bottlenecks, and assessing potential operational or financial impacts.
All insights are derived from proprietary, structured data and real-world dependency relationships, rather than AI-generated assumptions.
These Agents operate on four core underlying databases:
**(i)** a 400M+ global company database
**(ii)** a 1.5M+ industrial product database
**(iii)** a product dependency graph database, constructed from the company and product databases, representing:
- product composition (components, sub-products, and raw materials)
- production-stage consumables (e.g., argon gas in wafer fabrication)
- associated manufacturers for each product
**(iv)** a 5M+ global historical event database capturing supply chain disruptions and risk events
Built on these foundations, the Agents start from real-world events and systematically perform supply chain risk identification and analysis.
## Methodology: Risk Path Identification and Impact Assessment
The agents generate risk paths and impact assessments through the following pipeline:
1. Learning patterns from historical supply chain disruption events
2. Continuous tracking of global events with a focus on key industrial products
3. Matching real-time events with historical cases to identify risks affecting **中芯国际集成电路制造(成都)有限公司**
4. Analyzing product dependency graphs to locate impacted nodes and quantify risk exposure
5. Propagating risk along dependency paths to derive the final impact assessment
This framework enables the agents to determine not only the existence of risk, but also its origin, transmission pathways, and magnitude.
## Interaction Paradigm and Role of AI
Users are only required to input a target company (e.g., **中芯国际集成电路制造(成都)有限公司**), after which the data agents autonomously execute the full analytical pipeline.
Risk identification is grounded in real-world events.
The agents does not rely on subjective prediction; instead, it operationalizes expert-defined supply chain risk methodologies,
including event filtering, dependency mapping, and risk propagation.
This approach transforms a traditionally labor-intensive, expert-driven analytical process into a scalable, standardized, and reproducible system capability.
中芯国际集成电路制造(成都)有限公司 Profile
SMIC Chengdu Co., Ltd. is a subsidiary of Semiconductor Manufacturing International Corporation (SMIC), one of the leading semiconductor foundries in the world. Located in Chengdu, China, the company specializes in integrated circuit manufacturing, providing advanced technology and services to a global clientele. SMIC plays a crucial role in the semiconductor supply chain, offering solutions that cater to various industries, including consumer electronics, telecommunications, and automotive.
SupplyGraph.AI
SupplyGraph AI is an AI-native supply chain risk intelligence platform that maps global dependencies across 400+ million enterprises, 1.5 million industry products, and 5 million product dependency nodes.
Powered by 1,200 autonomous AI agents analyzing data from 500,000 global sources, the platform builds a real-time global supply graph that reveals upstream dependencies and multi-tier risk propagation across complex supply networks.