A Continuous-Field Photonic Chip Architecture: 3D In-Memory Computing with Dynamic Weight Modulation via PHAT B³D-HPA Architecture & PDMM Instruction Set Specification (v3.54 & v1.3)

This record contains the complete foundational documentation for the B³D-HPA (3D Body-High Performance Architecture) photonic computing framework. B³D-HPA Continu3. 5455. pdf: The primary architectural whitepaper detailing the dual-modality (deterministic/non-deterministic) framework, GPA (Geometric Polarization Arithmetic) logic, and the PIC (Physical Instruction Compiler) workflow. PDMMPhysical1. 3. pdf: The technical specification of the PDMM P-ISA (Physical Dual-Modality Mapping Instruction Set), detailing the instruction encoding, Jones Vector projection mappings, and physical-layer control parameters for hardware implementation. These documents together define the unified logic-to-physics compilation path for post-von Neumann photonic systems. This work presents the integration of B³D-HPA V3. 54, a continuous-wave photonic computing architecture based on physical hash addressing, with the PDMM Physical Dual-Modality Mapping Instruction Set (P-ISA) V1. 3, a unified deterministic-chaotic programming framework for 3D photonic media. The B³D-HPA architecture eliminates the fragility of global phase locking by strictly decoupling computation into two orthogonal domains: deterministic arithmetic operations are implemented via geometric polarization arithmetic and thulium ion energy-level logic, while controlled phase evolution is reserved for probabilistic AI regularization. Verified in V3. 54, the natural spatiotemporal orthogonality between thulium ion topological noise (S-Noise) and photodetector noise (P-Noise enables native physical-layer noise filtering without complex digital algorithms. PDMM P-ISA V1. 3 provides a physical-layer instruction model that directly maps high-level programs and tensor operations to light-matter interactions. Using wavelength-as-instruction addressing, it defines a deterministic skeleton based on intensity and energy-level states, and a chaotic semantic flow based on bounded fuzzy phase distributions, forming a unified instruction space compatible with large language models and deep neural networks. Together, B³D-HPA V3. 54 and PDMM P-ISA V1. 3 form a closed-loop, EDA-compatible photonic computing system, delivering orders-of-magnitude lower energy consumption than silicon-based arithmetic units, while providing a practical, mass-producible path for industrial-grade continuous-wave optical computing and embodied AI evolution.