mpw-005 / NPCM Nitride Poly Cut

the NPCM shapes of all of mpw-005

40 of 40 dies rendered.

20,166,002 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (77.7 MB in total, largest 4.3 MB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-005_NPCM_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-005_NPCM_reticle.png
the stitched reticle, full resolution
46.9 MB 35d222425bd590507a902fd5515b5d21c0ffda01f8ee9a88bdd7e396e7c31e5327753cd7 758a8c834b429796208b7aaf73e62efb0f43cfd3ad6c1662547ac5ed735b543d
mpw-005_NPCM_reticle_overview.png
the same image scaled to fit 4096 px
1.8 MB d707376273cd65e5d021c819ecce5e4163491e2902078f313a6ed981ed7a964cb579855f 846877229bc7864fe4e06873dffb41c6755b3045693ea37ea5516b82f72b5a0e
mpw-005_NPCM_tiles.json
where every tile belongs, and its checksum
13.6 kB 7dd9f849e54a800d504f9f38577669457914595f85555752d901cdeb5a31352f33d8c55f e211389b038fdfd6735d43ee5bc817fbe1b4b94b95d292ca8385325e816658c9
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 pll-based_capacitance-to-digital_converter_mpw4 7576×107760, 0 1.3 MBb1812ac5631a9633e06fa186f4595e80b6c976fe5d740728f30fddfa99cabf3f
A2 mpw5_cache 7576×107767576, 0 1.5 MB0663631151d5e4469190340b2c29272ed0a519d6a487383a376b7ec2fb77ef55
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 2.5 MBf09cf27f7542df42857a74bb75a4b061aa4c00bdf2db11db893a83edde563a8b
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 1.9 MB18a15514a1a97026a58aca29056682df2f71a0c43da1d0541d1ffe9abf25b324
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 1.8 MBd4c1ced2fb21598cbb4a2f767ba034b6e4631add54b96af6700b056620cbca9a
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 2.3 MB3731c57e4e6fad885a3e02e4aa2cf02e67c6ed659177b0d138c197bf613174ca
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 1.5 MBddc93a2df80552392caba8aaec6b711938e6f7027e574befe8cf656d754c0b48
B2 microwatt_mpw5 7576×107767576, 10776 4.3 MBed4a9c0bbde082398efd430f2c2b1e08c8e72f373452a3aa0c056ac24ecbaf7f
B3 armleo_gpio_mpw5 7576×1077615152, 10776 1.7 MB6f30b202015934859579099890491c179133de45d4b51debd59bf385bf9861b0
B4 fabulous_efpga 7576×1077622728, 10776 3.7 MBd27dcebd088ef3ce0a692f1470d3b242b50ce50dc71088e499459bab30f3849a
B5 i2c_controller 7576×1077630304, 10776 1.4 MBccc315082de8d85b735ad99c6fe570b7fac9fe1e3e1886b5c4a49dded3f0cfe9
B6 lbist-mbist 7576×1077637880, 10776 2.3 MB8a0692e11ab931886f9c194fafc69d32a1a825ecd1f4d1afaa4ff9b0472fe031
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 3.2 MB14a7cd90bf18a3e2ff00d713f15a508c8401a14371d9bcb57e4882c547f0a350
C1 riscduino-qcore__q0_ 7576×107760, 21552 3.2 MB804eff2c5d74de013887ffe718a6b57e965e05f825e9fd96578d5cc4bce0f0ac
C2 high_speed_vco 7576×107767576, 21552 1.3 MBbacb1905bb12c3e5e42c0b38a256555c4e70451d6eae41387b3aa2108268562a
C3 openfasoc-cryo-gen 7576×1077615152, 21552 1.4 MBee9d2048b2f220befbb94fa0f89dc2a85c186911717788c2f7288791b4098094
C4 microwave_signal_generator 7576×1077622728, 21552 1.3 MB53de952f43b33682ca0b44d71d57fb73fc4de9c718250c0aae30b963904e7246
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 2.3 MB52a940bd3b5c0d2d4e32fbfd67e2d191801e55ed7850491603773a63ae747121
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 1.3 MB0d6a0148af07faa00c1de003907c0b1cd54fc70617379d41544bdead23ddbb8f
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 1.3 MB527a5b450744f13ee7845a3e07bf82d0b4a3ca4ebb4b8f4ce5046be269ad4c44
D2 azadi_iii 7576×107767576, 32328 2.6 MB9ef15dd70fa332170919f7bf2468af0b01f518dfe44b9f95af5fb82a69c3ab08
D3 tia_for_physic_experiments 7576×1077615152, 32328 1.3 MBe1a45c6a6d3284b2a710ab80ef8db2d1291424cca56a16793027fa80288564ef
D4 qf105 7576×1077622728, 32328 2.7 MB4428f6679faaba276f2564f12e3c2f99de92fa3b6a7529a1a7bbc156524184f7
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 1.4 MB9621ef0b59af206da15057509fb5295502342875408d3a5ce014e276f6f86986
D6 junga_soc_mpw5 7576×1077637880, 32328 2.0 MB25bbd3c45a051c054f568e1ef6d1704ea1434bfdc284841061004d1ace1e22fa
D7 nand_flash_mpw-5 7976×1077645456, 32328 1.4 MBa778f135ecc2fba6338a56d2577e313c6c4ca67d0d4020b929cd4c115f9bd227
E1 kasirga-c0-mpw-5 7576×107760, 43104 2.0 MB9d6b8302bf7d3ffa2242e62e1d1f8801089b6b91b8b9c111b1cc604a5dd14bc1
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 2.5 MB1f0c488dfcee407782db54c2551db8e3dd6ff383ce9b056b73c2bc283582ee6d
E3 wirelessenergyharvesting 7576×1077615152, 43104 1.3 MBa29d4ee2aa52a86cf0f74ef9d17ec2123e2bab48244215c511107bbf85c4bfef
E4 caravel_raster_engine 7576×1077622728, 43104 3.0 MB504f93d7bb84552103b3254f00f8e7a5afc039d5cefb89907f7ad708e3641f55
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 1.3 MB445d511667ffd6da0b08c0efacece488cd7654372cfb108f454e93dbd6806b70
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 1.5 MB8e63c8aaa04a33a91ebcfadf838379a1730f4dfd865a828213a2e22c93c109ed
E7 alu 7976×1077645456, 43104 1.4 MB50f08d47f563810374d4139f12d0f14863db478b9a8223103dd358451e8997fb
F1 4ft4 7576×111760, 53880 1.8 MBa33b11f461dc29294fb4c0c045024a6492006c7e432b8f7d6dcdc32881ba925a
F2 rng_chaos 7576×111767576, 53880 1.4 MBc11d0899bfe7ef93e36b0eaeccee5690579ec5fca4681068929d623f2746bc63
F3 uetrv-ecore 7576×1117615152, 53880 2.1 MB15f9082af52edb771794088da0f3d14edb0dfb9bd22da140cc771f27a330fe37
F4 systolic_array 7576×1117622728, 53880 1.5 MBef01525ccae1cd95a337d873364c1c998e9c501bad6877860671c3d74cdab1db
F5 reram_test 7576×1117630304, 53880 1.3 MBb9405d06d9764636c87bf7a246dc525bb2aff5dc6aed2ab8dd29feb87ad92ed8
F6 secure_memory 7576×1117637880, 53880 1.6 MBb1ad414a19efb77f160409196871c345716c0fc5008fec7f38374ce5105323ba
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 1.5 MB040aceb6596a7d358845adf29d6d6d6e41972218949177e65c5ebdcb26aadd1b

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About NPCM on mpw-005

Resolved: reticle 5CS8011AC, run mpw-005, mask NPCM

Mask layer
NPCM — Nitride Poly Cut
Process step
78 — Nitride poly cut mask
Drawn layer
95:20
Layer on the mask-layer GDS
49:0
Used in SKY130
yes

mpw-005 — SKY130 MPW-5

Foundry product
EF MPW#5 (+ 'With ReRAM' split lot)
Reticle set
5CS8011AC
Run date
2022-03-21
Submissions closed
2022-03-21
Projects
74
Fab lot
4229389
Identification
confirmed — Lot records: EF MPW#5, lot 4229389 = 'SKY130 MPW 5'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-5 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-005/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.